Banded Dotterel Moult Study

Exploration of dataset

Authors

Affiliations

Luke Eberhart-Hertel

Department of Ornithology, Max Planck Institute for Biological Intelligence, Seewiesen, Germany

Bashar Jarayseh

Department of Ornithology, Max Planck Institute for Biological Intelligence, Seewiesen, Germany

Ailsa Howard

South Bay Banded Dotterel Project, Kaikoura, New Zealand

Ted Howard

South Bay Banded Dotterel Project, Kaikoura, New Zealand

Tony Habraken

Port Waikato Banded Dotterel Project, Port Waikato, New Zealand

Emma Williams

Department of Conservation, Christchurch, New Zealand

Colin O`Donnell

Department of Conservation, Christchurch, New Zealand

Bart Kempenaers

Department of Ornithology, Max Planck Institute for Biological Intelligence, Seewiesen, Germany

Published

January 28, 2025

Main conclusions since last meeting in Nov, 2024

Breeding plumage

Sex difference in maximum rufous band score (strong effect) Go to Results
- Males have a more full and immaculate rufous breast band than females
No age effect on maximum rufous band score Go to Results
Migratory-status difference in maximum rufous band score (weak, albiet significant effect) Go to Results
- Residents of both sexes have slightly more full and immaculate rufous breast bands than migrants

Pre-basic moult

No sex-specific timing of the pre-basic moult Go to Results
No migratory-status-specific timing of the pre-basic moult Go to Results
Age-specific timing of pre-basic moult (strong effect) Go to Results
- younger birds initiate their pre-basic moult earlier than older birds

Links to breeding activity

No effect of maximum rufous band score on prolonged breeding Go to Results
No effect of maximum rufous band score on early breeding Go to Results
No effect of prolonged breeding on maximum rufous band score Go to Results
No effect of prolonged breeding and timing of moult Go to Results
Positive relationship between the date when an individual was last seen with its maximum breeding plumage and the date when it was last observed breeding Go to Results
No age effect on prolonged breeding Go to Results
No age effect on early breeding Go to Results

Exploration of moult data extracted from photos

Note: data last modified 7-Nov-2024

This dataset contains the scored moult data for all usable photos from the 2021-2022, 2022-2023, and 2023-2024 breeding seasons at Kaikoura

check max and min dates for each season

# A tibble: 3 × 4
  season n_obs min_date   max_date  
   <dbl> <int> <date>     <date>    
1   2021   710 2021-08-02 2022-04-22
2   2022   614 2022-07-30 2023-04-14
3   2023   647 2023-07-01 2024-04-23

summarize the number of individuals in the dataset

[1] 94

assess how many individuals have more than a single season of data

[1] 63

summarise the number of usable photos for each individual across the three seasons

the final dataset

Code

dat %>% 
  datatable(class = 'cell-border stripe', rownames = FALSE, filter = 'top')

Plot the sampling distribution of Ailsa’s photos across the seasons

The “score” value is based on a scale from 1 to 7 describing the immaculate-ness and full-ness of the rufous breast band seen in the photos (Kok, Hogan, and Piersma (2020))

2021-2022 season data

2022-2023 season data

2023-2024 season data

Sources of individual variation in the maximum score of the rufous band

Sex-specific differences in breeding plumage

data wrangle for sex model - note that the max plumage score recorded during the core breeding months (Aug-Dec) is extracted for each individual for each year (“max_breeding_score”).

Code

# extract the core breeding months (i.e., when presumably all birds are at their maximum breeding plumage), and determine the maximum score for each individual.
ind_breeding_scores <-
  dat %>% 
  mutate(score = as.numeric(score)) %>% 
  mutate(breeding_season = ifelse(month(date) %in% c(8, 9, 10, 11, 12), 1, 0)) %>% 
  filter(breeding_season == 1) %>% 
  group_by(season, rings_comb, sex) %>% #filter(rings_comb == "RRBY") %>% select(score, date)
  summarise(max_breeding_score = max(score),
            date_of_max_score = date[which.max(score)],
            mode_breeding_score = get_mode(score)) %>% 
  ungroup() %>% 
  mutate(max_breeding_score_mean = round((max_breeding_score + mode_breeding_score)/2)) %>% 
  mutate(date_J_of_max_score = as.numeric(format(date_of_max_score + 181, "%j")))

Assess sample sizes of each sex

# A tibble: 2 × 2
  sex   `n_distinct(rings_comb)`
  <chr>                    <int>
1 F                           49
2 M                           35

mixed model with individual and season as random intercepts and sex as fixed effect

Code

# linear mixed model for the difference in max score between sexes
mod_max_score <- lmer(max_breeding_score ~ sex +
                        (1 | rings_comb) + (1 | season), 
                      data = ind_breeding_scores)

Males tend to have a more full and immaculate rufous bands than females, however there is substantial overlap in their distributions.

sex-specific breeding plumage distributions

effect-size table for sex-specific breeding plumage model

Banded Dotterel rufous band score	Mean estimate	95% confidence interval
Fixed effects 𝛽 (standardized)
Male (Sex)	1.01	[0.73, 1.27]
Partitioned 𝑹²
Total Marginal 𝑹²	0.29	[0.17, 0.41]
Sex	0.29	[0.17, 0.41]
Total Conditional 𝑹²	0.51	[0.33, 0.66]
Random effects 𝜎²
Individual	0.17	[0.04, 0.31]
Year	0.03	[0, 0.13]
Residual	0.42	[0.31, 0.57]
Adjusted repeatability 𝑟
Individual	0.27	[0.07, 0.46]
Year	0.04	[0, 0.17]
Residual	0.69	[0.49, 0.9]
Sample sizes 𝑛
Years	3
Individuals	83
Observations	164

Sex-specific breeding plumage model predictions

Age-and sex-specific variation in breeding plumage

data wrangle for sex-age model. Standardize max_breeding_score according to sex to make males and females to make them comparable across ages (i.e., does max_breeding_score change across age while accounting for underlying sex differences?) Only applied to known-aged birds.

Code

# extract the core breeding months (i.e., when presumably all birds are at their maximum breeding plumage), and determine the maximum score for each individual.
ind_breeding_scores_age <- 
  dat %>% 
  mutate(score = as.numeric(score)) %>% 
  mutate(breeding_season = ifelse(month(date) %in% c(8, 9, 10, 11, 12), 1, 0)) %>% 
  filter(breeding_season == 1) %>% 
  group_by(season, rings_comb, sex, age) %>% 
  summarise(max_breeding_score = max(score)) %>% 
  ungroup() %>%
  group_by(sex) %>% 
  mutate(std_max_breeding_score = (max_breeding_score - mean(max_breeding_score, na.rm = TRUE)) / sd(max_breeding_score, na.rm = TRUE)) %>%
  filter(!is.na(age))

Assess sample sizes of each sex for individuals with known age

Code

ind_breeding_scores_age %>% 
  group_by(sex) %>% 
  summarise(n_distinct(rings_comb))

# A tibble: 2 × 2
  sex   `n_distinct(rings_comb)`
  <chr>                    <int>
1 F                           14
2 M                           10

mixed model of standardized max breeding score (by sex) with individual as random intercept and age as fixed effect (Note: we tried to include season as an additional random intercept, but the low sample size of repeated measures across seasons caused convergence issues of the model)

Code

# linear mixed model for the difference in max score between sexes across age
mod_max_score_age <- 
  lmer(std_max_breeding_score ~ age +
         (1 | rings_comb), 
       data = ind_breeding_scores_age)

effect-size table for sex- and age-specific variation in breeding plumage

no effect of age on the maximum extent of an individual’s rufous band score while controlling for underlying sex-specific variation

Banded Dotterel rufous band score	Mean estimate	95% confidence interval
Fixed effects 𝛽 (standardized)
Age	0.09	[-0.24, 0.37]
Partitioned 𝑹²
Total Marginal 𝑹²	0.01	[0, 0.13]
Age	0.01	[0, 0.13]
Total Conditional 𝑹²	0.38	[0.01, 0.72]
Random effects 𝜎²
Individual	0.27	[0, 0.66]
Residual	0.45	[0.2, 0.75]
Adjusted repeatability 𝑟
Individual	0.37	[0, 0.72]
Residual	0.63	[0.28, 1]
Sample sizes 𝑛
Years	3
Individuals	24
Observations	42

age-specific breeding plumage model predictions

Migratory status-and sex-specific variation in breeding plumage

data wrangle for sex-migratory status model - only applied to birds with known migratory status based on winter resightings

Code

# extract the core breeding months (i.e., when presumably all birds are at their maximum breeding plumage), and determine the maximum score for each individual.
ind_breeding_scores_status <- 
  dat %>% 
  mutate(score = as.numeric(score)) %>% 
  mutate(breeding_season = ifelse(month(date) %in% c(8, 9, 10, 11, 12), 1, 0)) %>% 
  filter(breeding_season == 1) %>% 
  group_by(season, rings_comb, sex, age, migratory_status) %>% 
  summarise(max_breeding_score = max(score)) %>% 
  ungroup() %>%
  group_by(sex) %>% 
  mutate(std_max_breeding_score = (max_breeding_score - mean(max_breeding_score, na.rm = TRUE)) / sd(max_breeding_score, na.rm = TRUE)) %>% 
  filter(migratory_status %in% c("M", "R"))

Assess sample sizes of each sex and migratory status (for birds with known status)

Code

# Assess sample sizes of each sex and season 
# (13 females (2 migrant, 11 resident), 8 males (3 migrant, 5 resident))
ind_breeding_scores_status %>% 
  group_by(sex, migratory_status) %>% 
  summarise(n_distinct(rings_comb))

# A tibble: 4 × 3
# Groups:   sex [2]
  sex   migratory_status `n_distinct(rings_comb)`
  <chr> <chr>                               <int>
1 F     M                                       2
2 F     R                                      11
3 M     M                                       3
4 M     R                                       5

mixed model of standardized max breeding score (by sex) with individual as random intercept and migratory status as fixed effect (Note: we tried to include season as an additional random intercept, but the low sample size of repeated measures across seasons caused convergence issues of the model)

Code

# linear mixed model for the difference in max score between sexes
mod_max_score_status <- 
  lmer(std_max_breeding_score ~ migratory_status + 
         (1 | rings_comb), 
       data = ind_breeding_scores_status)

effect-size table and plot for sex- and migratory-status breeding plumage

no effect of migratory status on an individual’s rufous band score while controlling for underlying sex-specific variation

Banded Dotterel rufous band score	Mean estimate	95% confidence interval
Fixed effects 𝛽 (standardized)
Resident (Migratory Status)	0.55	[0.04, 1.16]
Partitioned 𝑹²
Total Marginal 𝑹²	0.10	[0, 0.34]
Migratory Status	0.10	[0, 0.34]
Total Conditional 𝑹²	0.23	[0.01, 0.61]
Random effects 𝜎²
Individual	0.08	[0, 0.38]
Residual	0.51	[0.23, 0.76]
Adjusted repeatability 𝑟
Individual	0.16	[0, 0.54]
Residual	0.84	[0.46, 1]
Sample sizes 𝑛
Years	3
Individuals	21
Observations	42

migratory status-specific breeding plumage model predictions

Sources of individual variation in the timing of pre-basic moult

Within-individual moult dynamics across sexes

wrangle data by calculating the individual proportional moult scores by comparing each score to a given individual’s max (determined in previous chunk). Piece apart sex-specific variation in the timing of pre-basic moult

Assess sample sizes of each sex

# A tibble: 2 × 2
  sex   `n_distinct(rings_comb)`
  <chr>                    <int>
1 F                           49
2 M                           32

mixed effects binomial model assessing sex-differences in the timing of pre-basic moult while accounting for bird- and year-specific random variation in the timing of pre-basic moult (i.e., random slope model of date for individual and year).

Code

mod_moult_sex_bird_RIS <- 
  lme4::glmer(prop_molt_score_max ~ 
                date_J + sex + 
         (date_J | ring_season),
       data = ind_prop_molt_scores, 
       family = binomial)

Characteristic	log(OR)¹	95% CI¹	p-value
(Intercept)	22	18, 26	<0.001
Date	-0.10	-0.12, -0.08	<0.001
Sex
F	—	—
M	-0.03	-0.80, 0.75	>0.9
¹ OR = Odds Ratio, CI = Confidence Interval

Within-individual moult dynamics across migratory status

# A tibble: 2 × 2
  migratory_status `n_distinct(rings_comb)`
  <chr>                               <int>
1 M                                       5
2 R                                      16

Code

# mixed effects binomial model comparing migratory status and date effect on the changes in moult scores
mod_moult_status_bird_RIS <- 
  lme4::glmer(prop_molt_score_max ~ 
                date_J + migratory_status +
         (date_J|ring_season),
       data = ind_prop_molt_scores_status, 
       family = binomial)

Characteristic	log(OR)¹	95% CI¹	p-value
(Intercept)	31	17, 44	<0.001
Date	-0.14	-0.20, -0.08	<0.001
Migratory status
M	—	—
R	-0.71	-2.7, 1.2	0.5
¹ OR = Odds Ratio, CI = Confidence Interval

Within-individual moult dynamics across ages

# A tibble: 6 × 2
    age `n_distinct(rings_comb)`
  <dbl>                    <int>
1     1                        6
2     2                        4
3     3                       12
4     4                       11
5     5                        4
6     6                        2

Code

# mixed effects binomial model comparing migratory status and date effect on the changes in moult scores
mod_moult_age_bird_RIS <- 
  lme4::glmer(prop_molt_score_max ~ 
                date_J + age +
         (date_J | ring_season),
       data = ind_prop_molt_scores_age, 
       family = binomial)

Characteristic	log(OR)¹	95% CI¹	p-value
(Intercept)	53	18, 88	0.003
Date	-0.28	-0.47, -0.10	0.003
Age	2.2	0.61, 3.7	0.006
¹ OR = Odds Ratio, CI = Confidence Interval

inspect the random slopes against the raw data from photos - potential issues with data coverage over the moulting period?

Breeding status analysis

Relationships between breeding schedule and plumage

effect of breeding plumage score on prolonged breeding

do individuals with a fuller breeding plumage (for their sex) breed later than the rest of the population? No effect

Code

mod_max_score_last_breeding_date <- 
  lme4::lmer(last_breeding_date_J_snum ~ 
                 std_max_breeding_score + 
         (1 | rings_comb) + (1 | season),
       data = ind_prop_molt_scores_last_breeding_date)

tbl_regression(mod_max_score_last_breeding_date, intercept = TRUE, 
               label = list(std_max_breeding_score ~ "max. breeding plumage score (sex-scaled)"))

Characteristic	Beta	95% CI¹
(Intercept)	10	5.8, 15
max. breeding plumage score (sex-scaled)	-1.5	-5.9, 2.9
¹ CI = Confidence Interval

Code

ggplot() +
  geom_line(data = as.data.frame(allEffects(mod_max_score_last_breeding_date))$std_max_breeding_score,
            aes(x = std_max_breeding_score, y = fit)) +
  geom_ribbon(data = as.data.frame(allEffects(mod_max_score_last_breeding_date))$std_max_breeding_score,
              aes(x = std_max_breeding_score, ymax = upper, ymin = lower),
              lwd = 1, alpha = 0.25) +
  geom_point(data = ind_prop_molt_scores_last_breeding_date,
              aes(x = std_max_breeding_score, y = last_breeding_date_J_snum),
              alpha = 1, shape = 20) +
  theme(text = element_text(family = "Verdana"),
        axis.ticks = element_blank(),
        plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"),
        panel.spacing = unit(1, "cm"),
        panel.background = element_rect(fill = 'transparent', color = NA),
        plot.background = element_rect(fill = 'transparent', color = NA), 
        panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"),
        panel.grid.minor = element_blank(),
        panel.grid.major.x = element_line(size = 0.25, lineend = "round", colour = "grey90")) +
  ylab("year-scaled date of last breeding attempt") +
  xlab("sex-scaled max. breeding plumage score")

effect of breeding plumage score on early breeding

do individuals with a fuller breeding plumage (for their sex) breed earlier than the rest of the population? No effect

Code

mod_max_score_first_breeding_date <- 
  lme4::lmer(first_breeding_date_J_snum ~ 
                 std_max_breeding_score +
         (1 | rings_comb) + (1 | season),
       data = ind_prop_molt_scores_first_breeding_date)

tbl_regression(mod_max_score_first_breeding_date, intercept = TRUE, 
               label = list(std_max_breeding_score ~ "max. breeding plumage score (sex-scaled)"))

Characteristic	Beta	95% CI¹
(Intercept)	0.66	-4.7, 6.0
max. breeding plumage score (sex-scaled)	-2.9	-7.8, 1.9
¹ CI = Confidence Interval

Code

ggplot() +
  geom_line(data = as.data.frame(allEffects(mod_max_score_first_breeding_date))$std_max_breeding_score,
            aes(x = std_max_breeding_score, y = fit)) +
  geom_ribbon(data = as.data.frame(allEffects(mod_max_score_first_breeding_date))$std_max_breeding_score,
              aes(x = std_max_breeding_score, ymax = upper, ymin = lower),
              lwd = 1, alpha = 0.25) +
  geom_point(data = ind_prop_molt_scores_first_breeding_date,
              aes(x = std_max_breeding_score, y = first_breeding_date_J_snum),
              alpha = 1, shape = 20) +
  theme(text = element_text(family = "Verdana"),
        axis.ticks = element_blank(),
        plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"),
        panel.spacing = unit(1, "cm"),
        panel.background = element_rect(fill = 'transparent', color = NA),
        plot.background = element_rect(fill = 'transparent', color = NA), 
        panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"),
        panel.grid.minor = element_blank(),
        panel.grid.major.x = element_line(size = 0.25, lineend = "round", colour = "grey90")) +
  ylab("year-scaled date of first breeding attempt") +
  xlab("sex-scaled max. breeding plumage score")

effect of prolonged breeding on breeding plumage score

do individuals with a late breeding attempts tend to have a fuller breeding plumage? No effect

Code

mod_last_breeding_date_max_score <- 
  lme4::lmer(std_max_breeding_score ~ 
                 last_breeding_date_J_snum +
         (1 | rings_comb) + (1 | season),
       data = ind_prop_molt_scores_last_breeding_date)

tbl_regression(mod_last_breeding_date_max_score, intercept = TRUE, 
               label = list(last_breeding_date_J_snum ~ "date last seen breeding (year-scaled)"))

Characteristic	Beta	95% CI¹
(Intercept)	0.00	-0.24, 0.25
date last seen breeding (year-scaled)	0.00	-0.01, 0.00
¹ CI = Confidence Interval

Code

ggplot() +
  geom_line(data = as.data.frame(allEffects(mod_last_breeding_date_max_score))$last_breeding_date_J_snum,
            aes(x = last_breeding_date_J_snum, y = fit)) +
  geom_ribbon(data = as.data.frame(allEffects(mod_last_breeding_date_max_score))$last_breeding_date_J_snum,
              aes(x = last_breeding_date_J_snum, ymax = upper, ymin = lower),
              lwd = 1, alpha = 0.25) +
  geom_point(data = ind_prop_molt_scores_last_breeding_date,
              aes(x = last_breeding_date_J_snum, y = std_max_breeding_score),
              alpha = 1, shape = 20) +
  theme(text = element_text(family = "Verdana"),
        axis.ticks = element_blank(),
        plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"),
        panel.spacing = unit(1, "cm"),
        panel.background = element_rect(fill = 'transparent', color = NA),
        plot.background = element_rect(fill = 'transparent', color = NA), 
        panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"),
        panel.grid.minor = element_blank(),
        panel.grid.major.x = element_line(size = 0.25, lineend = "round", colour = "grey90")) +
  xlab("year-scaled date of last breeding attempt") +
  ylab("sex-scaled max. breeding plumage score")

Relationships between breeding schedule and moult

effect of prolonged breeding on moult

do individuals with late breeding attempts have a delayed moult? No effect

Code

mod_last_breeding_date_prop_molt_mid <- 
  lmer(mid_point_date_J ~ last_breeding_date_J_snum + 
         (1 | rings_comb) + (1 | season),
       data = ind_prop_molt_scores_last_breeding_date)

tbl_regression(mod_last_breeding_date_prop_molt_mid, intercept = TRUE, 
               label = list(last_breeding_date_J_snum ~ "date last seen breeding (year-scaled)"))

Characteristic	Beta	95% CI¹
(Intercept)	168	157, 179
date last seen breeding (year-scaled)	0.14	-0.07, 0.36
¹ CI = Confidence Interval

Code

ggplot() +
  geom_line(data = as.data.frame(allEffects(mod_last_breeding_date_prop_molt_mid))$last_breeding_date_J_snum,
            aes(x = last_breeding_date_J_snum, y = fit)) +
  geom_ribbon(data = as.data.frame(allEffects(mod_last_breeding_date_prop_molt_mid))$last_breeding_date_J_snum,
              aes(x = last_breeding_date_J_snum, ymax = upper, ymin = lower),
              lwd = 1, alpha = 0.25) +
  geom_point(data = ind_prop_molt_scores_last_breeding_date,
              aes(x = last_breeding_date_J_snum, y = mid_point_date_J),
              alpha = 1, shape = 20) +
  theme(text = element_text(family = "Verdana"),
        axis.ticks = element_blank(),
        plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"),
        panel.spacing = unit(1, "cm"),
        panel.background = element_rect(fill = 'transparent', color = NA),
        plot.background = element_rect(fill = 'transparent', color = NA), 
        panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"),
        panel.grid.minor = element_blank(),
        panel.grid.major.x = element_line(size = 0.25, lineend = "round", colour = "grey90")) +
  xlab("year-scaled date of last breeding attempt") +
  ylab("mid-point date between\nlast max score photo and first photo during moult")

effect of prolonged breeding on prolonged retention of full plumage

do individuals with late breeding attempts retain their full breeding plumage longer? There is a positive relationship between the date when an individual was last seen with its maximum breeding plumage and the date when it was last observed breeding.

Code

mod_last_breeding_date_prop_molt_latest <- 
  lmer(latest_max_date_J ~ last_breeding_date_J_snum + 
        (1 | season),
       data = ind_prop_molt_scores_last_breeding_date)

tbl_regression(mod_last_breeding_date_prop_molt_latest, intercept = TRUE, 
               label = list(last_breeding_date_J_snum ~ "date last seen breeding (year-scaled)"))

Characteristic	Beta	95% CI¹
(Intercept)	156	141, 172
date last seen breeding (year-scaled)	0.37	0.17, 0.56
¹ CI = Confidence Interval

Code

ggplot() +
  geom_line(data = as.data.frame(allEffects(mod_last_breeding_date_prop_molt_latest))$last_breeding_date_J_snum,
            aes(x = last_breeding_date_J_snum, y = fit)) +
  geom_ribbon(data = as.data.frame(allEffects(mod_last_breeding_date_prop_molt_latest))$last_breeding_date_J_snum,
              aes(x = last_breeding_date_J_snum, ymax = upper, ymin = lower),
              lwd = 1, alpha = 0.25) +
  geom_point(data = ind_prop_molt_scores_last_breeding_date,
              aes(x = last_breeding_date_J_snum, y = latest_max_date_J),
              alpha = 1, shape = 20) +
  theme(text = element_text(family = "Verdana"),
        axis.ticks = element_blank(),
        plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"),
        panel.spacing = unit(1, "cm"),
        panel.background = element_rect(fill = 'transparent', color = NA),
        plot.background = element_rect(fill = 'transparent', color = NA), 
        panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"),
        panel.grid.minor = element_blank(),
        panel.grid.major.x = element_line(size = 0.25, lineend = "round", colour = "grey90")) +
  xlab("year-scaled date of last breeding attempt") +
  ylab("last date photographed with max score")

Relationships between age and breeding phenology

effect of age on prolonged breeding

do older individuals conclude their breeding season later? No

Code

mod_age_last_breeding_date <- 
  lme4::lmer(last_breeding_date_J_snum ~ age +
        (1 | rings_comb) + (1 | season),
       data = ind_prop_molt_scores_last_breeding_date)

tbl_regression(mod_age_last_breeding_date, intercept = TRUE, 
               label = list(age ~ "Age"))

Characteristic	Beta	95% CI¹
(Intercept)	13	-17, 43
Age	-0.83	-9.0, 7.4
¹ CI = Confidence Interval

Code

ggplot() +
  geom_line(data = as.data.frame(allEffects(mod_age_last_breeding_date))$age,
            aes(x = age, y = fit)) +
  geom_ribbon(data = as.data.frame(allEffects(mod_age_last_breeding_date))$age,
              aes(x = age, ymax = upper, ymin = lower),
              lwd = 1, alpha = 0.25) +
  geom_point(data = ind_prop_molt_scores_last_breeding_date,
              aes(x = age, y = last_breeding_date_J_snum),
              alpha = 1, shape = 20) +
  theme(text = element_text(family = "Verdana"),
        axis.ticks = element_blank(),
        plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"),
        panel.spacing = unit(1, "cm"),
        panel.background = element_rect(fill = 'transparent', color = NA),
        plot.background = element_rect(fill = 'transparent', color = NA), 
        panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"),
        panel.grid.minor = element_blank(),
        panel.grid.major.x = element_line(size = 0.25, lineend = "round", colour = "grey90")) +
  xlab("age") +
  ylab("year-scaled date of last breeding attempt")

effect of age on early breeding

do older individuals start breeding earlier? No

Code

mod_age_first_breeding_date <- 
  lme4::lmer(first_breeding_date_J_snum ~ age +
         (1 | rings_comb) + (1 | season),
       data = ind_prop_molt_scores_first_breeding_date)

tbl_regression(mod_age_first_breeding_date, intercept = TRUE, 
               label = list(age ~ "Age"))

Characteristic	Beta	95% CI¹
(Intercept)	-4.8	-35, 25
Age	2.3	-5.7, 10
¹ CI = Confidence Interval

Code

ggplot() +
  geom_line(data = as.data.frame(allEffects(mod_age_first_breeding_date))$age,
            aes(x = age, y = fit)) +
  geom_ribbon(data = as.data.frame(allEffects(mod_age_first_breeding_date))$age,
              aes(x = age, ymax = upper, ymin = lower),
              lwd = 1, alpha = 0.25) +
  geom_point(data = ind_prop_molt_scores_first_breeding_date,
              aes(x = age, y = first_breeding_date_J_snum),
              alpha = 1, shape = 20) +
  theme(text = element_text(family = "Verdana"),
        axis.ticks = element_blank(),
        plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"),
        panel.spacing = unit(1, "cm"),
        panel.background = element_rect(fill = 'transparent', color = NA),
        plot.background = element_rect(fill = 'transparent', color = NA), 
        panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"),
        panel.grid.minor = element_blank(),
        panel.grid.major.x = element_line(size = 0.25, lineend = "round", colour = "grey90")) +
  xlab("age") +
  ylab("year-scaled date of first breeding attempt")

Meeting notes:

13-Nov-24: Bart, Luke, and Bashar via zoom

add the breeding data to the analysis: for each individual include the date of last breeding (i.e., last date seen with nest or brood) as a predictor of molt timing. Also do an analysis relating the timing of last breeding to the age of the individual. Bashar will send Luke the breeding data he received from Ted.
drop pre-October data from the molt timing analysis.
next steps: Bashar to eventually write up this study as a report (which could eventually be used as his MSc thesis if he wants). Priority will be on him writing up a proposal for his thesis (which has a concrete deadline and is graded). Bashar will present the study at our weekly Monday meeting in the first few months of 2025.

Relevent papers to read and consider for manuscript

Gilbert (2022): Late breeding season definitive prebasic molt in males, and late breeding season brood care by females, in central California Wilson’s warblers
Drobniak and Gudowska (2020): Seasonality, diet, and physiology as a predominant control factors of the moult dynamics in birds – a meta-analysis Figure 4 from their pre-print is relevant for the age-specific timing we see:

Thompson and Slack (1983): Molt-breeding overlap and timing of pre-basic molt in Texas Least Terns
Barta et al. (2008): Optimal moult strategies in migratory birds

References

Barta, Zoltan, John M McNamara, Alasdair I Houston, Thomas P Weber, Anders Hedenström, and Orsolya Fero. 2008. “Optimal Moult Strategies in Migratory Birds.” Philosophical Transactions of the Royal Society B: Biological Sciences 363 (1490): 211–29.

Drobniak, Szymon Marian, and Agnieszka Gudowska. 2020. “Seasonality, Diet, and Physiology as a Predominant Control Factors of the Moult Dynamics in Birds–a Meta-Analysis.”

Gilbert, William M. 2022. “Late Breeding Season Definitive Prebasic Molt in Males, and Late Breeding Season Brood Care by Females, in Central California Wilson’s Warblers.” Ecology and Evolution 12 (3): e8689.

Kok, Eva MA, Jerry A Hogan, and Theunis Piersma. 2020. “Experimental Tests of a Seasonally Changing Visual Preference for Habitat in a Long-Distance Migratory Shorebird.” Ethology 126 (7): 681–93.

Thompson, Bruce C, and R Douglas Slack. 1983. “Molt-Breeding Overlap and Timing of Pre-Basic Molt in Texas Least Terns.” Journal of Field Ornithology 54 (2): 187–90.

--- title: "Banded Dotterel Moult Study" subtitle: | Exploration of dataset date: "`r format(Sys.time(), '%H:%M %d %B, %Y')`" author: - name: Luke Eberhart-Hertel orcid: 0000-0001-7311-6088 email: luke.eberhart@bi.mpg.de url: https://www.bi.mpg.de/person/115852/2867 affiliations: - ref: bk - name: Bashar Jarayseh affiliations: - ref: bk - name: Ailsa Howard affiliations: - ref: ah - name: Ted Howard affiliations: - ref: ah - name: Tony Habraken affiliations: - ref: th - name: Emma Williams affiliations: - ref: ew - name: Colin O`Donnell affiliations: - ref: ew - name: Bart Kempenaers affiliations: - ref: bk affiliations: - id: bk number: 1 name: Department of Ornithology, Max Planck Institute for Biological Intelligence, Seewiesen, Germany - id: ah number: 2 name: South Bay Banded Dotterel Project, Kaikoura, New Zealand - id: th number: 2 name: Port Waikato Banded Dotterel Project, Port Waikato, New Zealand - id: ew number: 3 name: Department of Conservation, Christchurch, New Zealand format: html: toc: true code-fold: true code-tools: true self-contained: true highlight-style: github theme: Cosmo execute: warning: false cache: true editor_options: chunk_output_type: console bibliography: references.bib --- ```{r, include=FALSE} knitr::opts_chunk$set(cache = TRUE) options(repos = c(CRAN = "https://cran.rstudio.com")) ``` ```{r, message=FALSE, results='hide', warning=FALSE, cache=FALSE, include=FALSE} ## Prerequisites ### R packages # - The following packages are needed for analysis and can be easily installed from [CRAN](http://cran.r-project.org/) or GitHub by running the following code chunk: # a vector of all the packages needed in the project packages_required_in_project <- c("tidyverse", "readxl", "RMark", "RColorBrewer", "patchwork", "mapview", "lubridate", "extrafont", "here", "DT", "leaflet", "sf", "leafpop", "tsibble", "corrplot", "gghalves", "gam", "pscl", "gamlss", "gt", "lme4", "ggpattern", "gtsummary", "effects", "lattice", "rptR", "partR2", "broom.mixed", "forcats", "glmmTMB", "sysfonts", "showtext", "patchwork", "standardize") # of the required packages, check if some need to be installed new.packages <- packages_required_in_project[!(packages_required_in_project %in% installed.packages()[,"Package"])] # install all packages that are not locally available if(length(new.packages)) install.packages(new.packages) # load all the packages into the current R session lapply(packages_required_in_project, require, character.only = TRUE) # set the home directory to where the project is locally based (i.e., to find # the relevant datasets to import, etc. here::set_here() ``` ```{r, message=FALSE, results='hide', warning=FALSE, include=FALSE} ### Plotting themes # - The following plotting themes, colors, and typefaces are used throughout the project: # Find fonts from computer that you want. Use regular expressions to do this # For example, load all fonts that are 'verdana' or 'Verdana' extrafont::font_import(pattern = "[V/v]erdana", prompt = FALSE) # check which fonts were loaded extrafont::fonts() extrafont::fonttable() extrafont::loadfonts() # load these into R # # Add Google font Verdana # font_add_google("Lato", "Lato") # # # Automatically use showtext to render text # showtext_auto() # define the plotting theme to be used in subsequent ggplots luke_theme <- theme_bw() + theme( text = element_text(family = "Verdana"), legend.title = element_text(size = 10), legend.text = element_text(size = 8), axis.title.x = element_text(size = 10), axis.text.x = element_text(size = 8), axis.title.y = element_text(size = 10), axis.text.y = element_text(size = 8), strip.text = element_text(size = 10), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.ticks = element_line(linewidth = 0.5, colour = "grey40"), axis.ticks.length = unit(0.2, "cm"), panel.border = element_rect(linetype = "solid", colour = "grey"), legend.position.inside = c(0.1, 0.9) ) ``` ```{r, include=FALSE} # Define a custom mode function get_mode <- function(x) { uniq_vals <- unique(x) uniq_vals[which.max(tabulate(match(x, uniq_vals)))] } ``` ## Main conclusions since last meeting in Nov, 2024 ### Breeding plumage - Sex difference in maximum rufous band score (strong effect) [Go to Results](#sex-specific-differences-in-breeding-plumage) - Males have a more full and immaculate rufous breast band than females - No age effect on maximum rufous band score [Go to Results](#age-and-sex-specific-variation-in-breeding-plumage) - Migratory-status difference in maximum rufous band score (weak, albiet significant effect) [Go to Results](#migratory-status-and-sex-specific-variation-in-breeding-plumage) - Residents of both sexes have slightly more full and immaculate rufous breast bands than migrants ### Pre-basic moult - No sex-specific timing of the pre-basic moult [Go to Results](#within-individual-moult-dynamics-across-sexes) - No migratory-status-specific timing of the pre-basic moult [Go to Results](#within-individual-moult-dynamics-across-migratory-status) - Age-specific timing of pre-basic moult (strong effect) [Go to Results](#within-individual-moult-dynamics-across-ages) - younger birds initiate their pre-basic moult earlier than older birds ### Links to breeding activity - No effect of maximum rufous band score on prolonged breeding [Go to Results](#effect-of-breeding-plumage-score-on-prolonged-breeding) - No effect of maximum rufous band score on early breeding [Go to Results](#effect-of-breeding-plumage-score-on-early-breeding) - No effect of prolonged breeding on maximum rufous band score [Go to Results](#effect-of-prolonged-breeding-on-breeding-plumage-score) - No effect of prolonged breeding and timing of moult [Go to Results](#effect-of-prolonged-breeding-on-moult) - Positive relationship between the date when an individual was last seen with its maximum breeding plumage and the date when it was last observed breeding [Go to Results](#effect-of-prolonged-breeding-on-prolonged-retention-of-full-plumage) - No age effect on prolonged breeding [Go to Results](#effect-of-age-on-prolonged-breeding) - No age effect on early breeding [Go to Results](#effect-of-age-on-early-breeding) ## Exploration of moult data extracted from photos Note: data last modified 7-Nov-2024 This dataset contains the scored moult data for all usable photos from the 2021-2022, 2022-2023, and 2023-2024 breeding seasons at Kaikoura ```{r, include = FALSE} # import data with all columns as character (so that no auto-formatting is done by R) dat <- read.csv("/Users/leberhart/ownCloud/kemp_projects/bdot/moult/data/Dataset_Molt_Final - modified 7.11.2024.csv", colClasses = "character") %>% mutate(Rings_comb = str_sub(Rings_comb, 1, 4)) %>% rename_with(tolower) # convert all column headers to lowercase # check values of Date column for mistakes...looks good unique(dat$date) # check values of score column for mistakes...one row to fix unique(dat$score) # one row contains no data for the score (row 767)...needs to be fixed dat %>% filter(score == "") # check values of rings_comb column for mistakes...need to remove the non-unique combos (rows with XB and XW) unique(dat$rings_comb) dat %>% filter(rings_comb %in% c("XB", "XW")) dat <- dat %>% filter(!grepl("XB", rings_comb) & !grepl("XW", rings_comb)) # import sex, migratory status, and age at banding information dat_sexes <- read.csv("/Users/leberhart/ownCloud/kemp_projects/bdot/moult/data/sex_status_banding.csv", colClasses = "character") %>% mutate(Rings_comb = str_sub(Rings_comb, 1, 4)) %>% rename_with(tolower) %>% # convert all column headers to lowercase distinct() # checkout the sex-type data # number of birds with sex-type data dat_sexes %>% pull(rings_comb) %>% unique() %>% length() # identify duplicates...none, good dat_sexes[which(duplicated(dat_sexes)), ] # assess all birds with migratory status data dat_sexes %>% filter(migratory_status %in% c("R", "M")) # mutate the Date column into a date variable dat <- dat %>% mutate(date = paste(substring(dat$date, first = 7, last = 10), substring(date, first = 4, last = 5), substring(date, first = 1, last = 2), sep = "-") %>% as.Date()) %>% # subset to data with Molt == 1 filter(molt == 1) %>% # specify the season as the first calender year mutate(season = ifelse(month(date) < 7, year(date) - 1, year(date))) %>% # change to Julian date shifted for the Southern Hemisphere (1 = July 1) mutate(date_J = as.numeric(format(date + 181, "%j"))) %>% # join the sexes provided by Ailsa (note many-to-many is fine) left_join(., dat_sexes, by = "rings_comb", relationship = "many-to-many") %>% # remove individuals with unknown sex filter(sex != "U") %>% filter(sex != "N") %>% # add a ranking variable to sort the facets of the sampling distribution plots group_by(rings_comb) %>% mutate(n_photos = n(), n_scores = n_distinct(score)) %>% arrange(desc(n_scores)) %>% mutate( banding_date = paste(substring(banding_date, first = 7, last = 10), substring(banding_date, first = 4, last = 5), substring(banding_date, first = 1, last = 2), sep = "-") %>% as.Date(), season_ringed = ifelse(age_at_banding == "P" , ifelse(month(banding_date) < 7, year(banding_date) - 1, year(banding_date)), NA), age = ifelse(age_at_banding == "P", as.numeric(season - season_ringed), NA))# i should find a better way to calculate age ``` check max and min dates for each season ```{r, echo= FALSE} # check max and min dates for each season dat %>% group_by(season) %>% summarise(n_obs = n(), min_date = min(date), max_date = max(date)) ``` summarize the number of individuals in the dataset ```{r, echo = FALSE} dat %>% pull(rings_comb) %>% unique() %>% length() ``` assess how many individuals have more than a single season of data ```{r, echo = FALSE} dat %>% select(rings_comb, season) %>% distinct() %>% group_by(rings_comb) %>% summarise(n_seasons = n()) %>% arrange(desc(n_seasons)) %>% filter(n_seasons > 1) %>% nrow() ``` summarise the number of usable photos for each individual across the three seasons ```{r, echo=FALSE} dat %>% group_by(rings_comb, season) %>% summarise(n_photos = n()) %>% arrange(desc(n_photos)) %>% pivot_wider(., names_from = season, values_from = n_photos) %>% datatable(class = 'cell-border stripe', rownames = FALSE, filter = 'top') ``` the final dataset ```{r, echo=TRUE} dat %>% datatable(class = 'cell-border stripe', rownames = FALSE, filter = 'top') ``` ### Plot the sampling distribution of Ailsa's photos across the seasons The "score" value is based on a scale from 1 to 7 describing the immaculate-ness and full-ness of the rufous breast band seen in the photos (@kok2020experimental) ```{r, echo=FALSE} knitr::include_graphics("/Users/leberhart/ownCloud/kemp_projects/bdot/moult/figs/combined_figure_draft.png") ``` ```{r, include=FALSE} dat %>% select(sex, rings_comb, season, score, date) %>% group_by(sex, rings_comb, season) %>% summarise(n_unique_scores = n_distinct(score)) %>% arrange(desc(n_unique_scores)) ``` #### 2021-2022 season data ```{r, fig.height=9, echo=FALSE} # 2021-2022 season data ggplot(data = dat %>% filter(season == 2021 & sex == "F") %>% mutate(score = as.numeric(score), rings_comb_ = reorder(rings_comb, n_photos, decreasing = TRUE))) + geom_point(aes(y = 1, x = date, fill = score), pch = 21, color = "black", size = 3) + facet_wrap(. ~ rings_comb_, ncol = 1, strip.position = "right") + scale_fill_gradient(high = "#cc4c02", low = "white") + theme_bw() + theme(axis.title.y = element_blank(), axis.text.y = element_blank(), panel.grid.major.y = element_blank(), panel.grid.minor = element_blank(), axis.ticks = element_blank(), strip.text.y.right = element_text(angle = 0), panel.border = element_rect(linetype = "solid", colour = "grey90"), strip.background = element_rect(color = "grey90", fill = "grey90"), panel.grid.major.x = element_line(color = "grey30")) + scale_x_date(date_labels = "%W", expand = c(0.01, 0.01), date_breaks = "3 week", limits = c(as.Date("2021-07-05"), as.Date("2022-05-01"))) + xlab("week") + ggtitle("Females from the 2021-2022 breeding season in Kaikoura") ``` ```{r, fig.height=8, echo=FALSE} ggplot(data = dat %>% filter(season == 2021 & sex == "M") %>% mutate(score = as.numeric(score))) + geom_point(aes(y = 1, x = date, fill = score), pch = 21, color = "black", size = 3) + facet_wrap(. ~ reorder(rings_comb, n_photos, decreasing = TRUE), ncol = 1, strip.position = "right") + scale_fill_gradient(high = "#cc4c02", low = "white") + theme_bw() + theme(axis.title.y = element_blank(), axis.text.y = element_blank(), panel.grid.major.y = element_blank(), panel.grid.minor = element_blank(), axis.ticks = element_blank(), strip.text.y.right = element_text(angle = 0), panel.border = element_rect(linetype = "solid", colour = "grey90"), strip.background = element_rect(color = "grey90", fill = "grey90"), panel.grid.major.x = element_line(color = "grey30")) + scale_x_date(date_labels = "%W", expand = c(0.01, 0.01), date_breaks = "3 week", limits = c(as.Date("2021-07-05"), as.Date("2022-05-01"))) + xlab("week") + ggtitle("Males from the 2021-2022 breeding season in Kaikoura") ``` #### 2022-2023 season data ```{r, fig.height=10, echo=FALSE} # 2022-2023 season data (not as good coverage as the previous season) ggplot(data = dat %>% filter(season == 2022 & sex == "F") %>% mutate(score = as.numeric(score))) + geom_point(aes(y = 1, x = date, fill = score), pch = 21, color = "black", size = 3) + facet_wrap(. ~ reorder(rings_comb, n_photos, decreasing = TRUE), ncol = 1, strip.position = "right") + scale_fill_gradient(high = "#cc4c02", low = "white") + theme_bw() + theme(axis.title.y = element_blank(), axis.text.y = element_blank(), panel.grid.major.y = element_blank(), panel.grid.minor = element_blank(), axis.ticks = element_blank(), strip.text.y.right = element_text(angle = 0), panel.border = element_rect(linetype = "solid", colour = "grey90"), strip.background = element_rect(color = "grey90", fill = "grey90"), panel.grid.major.x = element_line(color = "grey30")) + scale_x_date(date_labels = "%W", expand = c(0.01, 0.01), date_breaks = "3 week", limits = c(as.Date("2022-07-05"), as.Date("2023-05-01"))) + xlab("week") + ggtitle("Females from the 2022-2023 breeding season in Kaikoura") ``` ```{r, fig.height=7, echo=FALSE} ggplot(data = dat %>% filter(season == 2022 & sex == "M") %>% mutate(score = as.numeric(score))) + geom_point(aes(y = 1, x = date, fill = score), pch = 21, color = "black", size = 3) + facet_wrap(. ~ reorder(rings_comb, n_photos, decreasing = TRUE), ncol = 1, strip.position = "right") + scale_fill_gradient(high = "#cc4c02", low = "white") + theme_bw() + theme(axis.title.y = element_blank(), axis.text.y = element_blank(), panel.grid.major.y = element_blank(), panel.grid.minor = element_blank(), axis.ticks = element_blank(), strip.text.y.right = element_text(angle = 0), panel.border = element_rect(linetype = "solid", colour = "grey90"), strip.background = element_rect(color = "grey90", fill = "grey90"), panel.grid.major.x = element_line(color = "grey30")) + scale_x_date(date_labels = "%W", expand = c(0.01, 0.01), date_breaks = "3 week", limits = c(as.Date("2022-07-05"), as.Date("2023-05-01"))) + xlab("week") + ggtitle("Males from the 2022-2023 breeding season in Kaikoura") ``` #### 2023-2024 season data ```{r, fig.height=10, echo=FALSE} # 2023-2024 season data ggplot(data = dat %>% filter(season == 2023 & sex == "F") %>% mutate(score = as.numeric(score))) + geom_point(aes(y = 1, x = date, fill = score), pch = 21, color = "black", size = 3) + facet_wrap(. ~ reorder(rings_comb, n_photos, decreasing = TRUE), ncol = 1, strip.position = "right") + scale_fill_gradient(high = "#cc4c02", low = "white") + theme_bw() + theme(axis.title.y = element_blank(), axis.text.y = element_blank(), panel.grid.major.y = element_blank(), panel.grid.minor = element_blank(), axis.ticks = element_blank(), strip.text.y.right = element_text(angle = 0), panel.border = element_rect(linetype = "solid", colour = "grey90"), strip.background = element_rect(color = "grey90", fill = "grey90"), panel.grid.major.x = element_line(color = "grey30")) + scale_x_date(date_labels = "%W", expand = c(0.01, 0.01), date_breaks = "3 week", limits = c(as.Date("2023-07-01"), as.Date("2024-05-01"))) + xlab("week") + ggtitle("Females from the 2023-2024 breeding season in Kaikoura") ``` ```{r, fig.height=6, echo=FALSE} ggplot(data = dat %>% filter(season == 2023 & sex == "M") %>% mutate(score = as.numeric(score))) + geom_point(aes(y = 1, x = date, fill = score), pch = 21, color = "black", size = 3) + facet_wrap(. ~ reorder(rings_comb, n_photos, decreasing = TRUE), ncol = 1, strip.position = "right") + scale_fill_gradient(high = "#cc4c02", low = "white") + theme_bw() + theme(axis.title.y = element_blank(), axis.text.y = element_blank(), panel.grid.major.y = element_blank(), panel.grid.minor = element_blank(), axis.ticks = element_blank(), strip.text.y.right = element_text(angle = 0), panel.border = element_rect(linetype = "solid", colour = "grey90"), strip.background = element_rect(color = "grey90", fill = "grey90"), panel.grid.major.x = element_line(color = "grey30")) + scale_x_date(date_labels = "%W", expand = c(0.01, 0.01), date_breaks = "3 week", limits = c(as.Date("2023-07-05"), as.Date("2024-05-01"))) + xlab("week") + ggtitle("Males from the 2023-2024 breeding season in Kaikoura") ``` ## Sources of individual variation in the maximum score of the rufous band ### Sex-specific differences in breeding plumage data wrangle for sex model - note that the max plumage score recorded during the core breeding months (Aug-Dec) is extracted for each individual for each year ("max_breeding_score"). ```{r} # extract the core breeding months (i.e., when presumably all birds are at their maximum breeding plumage), and determine the maximum score for each individual. ind_breeding_scores <- dat %>% mutate(score = as.numeric(score)) %>% mutate(breeding_season = ifelse(month(date) %in% c(8, 9, 10, 11, 12), 1, 0)) %>% filter(breeding_season == 1) %>% group_by(season, rings_comb, sex) %>% #filter(rings_comb == "RRBY") %>% select(score, date) summarise(max_breeding_score = max(score), date_of_max_score = date[which.max(score)], mode_breeding_score = get_mode(score)) %>% ungroup() %>% mutate(max_breeding_score_mean = round((max_breeding_score + mode_breeding_score)/2)) %>% mutate(date_J_of_max_score = as.numeric(format(date_of_max_score + 181, "%j"))) ``` ```{r, include=FALSE} ind_breeding_scores %>% mutate(score_diff = max_breeding_score - mode_breeding_score) %>% ggplot() + geom_histogram(aes(score_diff)) ind_breeding_scores %>% mutate(score_diff = max_breeding_score - mode_breeding_score) %>% arrange(desc(score_diff)) %>% select(season, rings_comb, sex, max_breeding_score, mode_breeding_score, date_of_max_score, score_diff) # three individuals with a difference in max and mode score greater than 2 dat %>% filter(rings_comb == "RWBB" & season == "2021") %>% select(rings_comb, season, date, sex, n_photos, score) %>% arrange(date) dat %>% filter(rings_comb == "RWBG" & season == "2021") %>% select(rings_comb, season, date, sex, n_photos, score) %>% arrange(date) dat %>% filter(rings_comb == "RRWY" & season == "2022") %>% select(rings_comb, season, date, sex, n_photos, score) %>% arrange(date) dat %>% filter(rings_comb == "RRBY" & season == "2021") %>% select(rings_comb, season, date, sex, n_photos, score) %>% arrange(date) ``` Assess sample sizes of each sex ```{r, echo=FALSE} ind_breeding_scores %>% group_by(sex) %>% summarise(n_distinct(rings_comb)) ``` mixed model with individual and season as random intercepts and sex as fixed effect ```{r, fold-show} # linear mixed model for the difference in max score between sexes mod_max_score <- lmer(max_breeding_score ~ sex + (1 | rings_comb) + (1 | season), data = ind_breeding_scores) ``` ```{r, eval=FALSE, include=FALSE} tbl_regression(mod_max_score, intercept = TRUE, label = list(sex ~ "Sex")) # Derive confidence intervals of effect sizes from parametric bootstrapping tidy_mod_max_score <- tidy(mod_max_score, conf.int = TRUE, conf.method = "boot", nsim = 1000) # run rptR to obtain repeatabilities of random effects rpt_mod_max_score <- rpt(max_breeding_score ~ sex + (1 | rings_comb) + (1 | season), grname = c("rings_comb", "season", "Fixed"), data = ind_breeding_scores, datatype = "Gaussian", nboot = 1000, npermut = 1000, ratio = TRUE, adjusted = TRUE, ncores = 4, parallel = TRUE) # run partR2 on each model to obtain marginal R2, parameter estimates, and beta # weights R2m_mod_max_score <- partR2(mod_max_score, partvars = c("sex"), R2_type = "marginal", nboot = 1000, CI = 0.95, max_level = 1) R2c_mod_max_score <- partR2(mod_max_score, partvars = c("sex"), R2_type = "conditional", nboot = 1000, CI = 0.95, max_level = 1) stats_mod_max_score <- list(mod = mod_max_score, tidy = tidy_mod_max_score, rptR = rpt_mod_max_score, partR2m = R2m_mod_max_score, partR2c = R2c_mod_max_score, data = ind_breeding_scores) ``` ```{r, include=FALSE} # saveRDS(stats_mod_max_score, file = "out/stats_mod_max_score.rds") stats_mod_max_score <- readRDS(file = "/Users/leberhart/ownCloud/kemp_projects/bdot/moult/out/stats_mod_max_score.rds") ``` ```{r, include=FALSE} #### Table of effect sizes ---- # Retrieve sample sizes sample_sizes <- ind_breeding_scores %>% ungroup() %>% summarise(Year = n_distinct(season), Individual = n_distinct(rings_comb), Observations = nrow(.)) sample_sizes <- as.data.frame(t(as.data.frame(sample_sizes))) %>% rownames_to_column("term") %>% rename(estimate = V1) %>% mutate(stat = "n") # clean model component names mod_comp_names <- data.frame(comp_name = c("Male (Sex)", "Total Marginal \U1D479\U00B2", "Sex", "Total Conditional \U1D479\U00B2", "Individual", "Year", "Residual", "Individual", "Year", "Residual", "Years", "Individuals", "Observations")) # Fixed effect sizes (non-standardized) fixefTable <- stats_mod_max_score$tidy %>% dplyr::filter(effect == "fixed") %>% dplyr::select(term, estimate, conf.low, conf.high) %>% as.data.frame() %>% mutate(stat = "fixed") # Fixed effect sizes (standardized) fixef_bw_Table <- stats_mod_max_score$partR2m$BW %>% as.data.frame() %>% mutate(stat = "fixed_bw") %>% rename(conf.low = CI_lower, conf.high = CI_upper) # Semi-partial R2 estimates R2Table <- bind_rows(stats_mod_max_score$partR2m$R2, stats_mod_max_score$partR2c$R2[1,]) %>% dplyr::select(term, estimate, CI_lower, CI_upper) %>% as.data.frame() %>% mutate(stat = "partR2") %>% rename(conf.low = CI_lower, conf.high = CI_upper) # Random effects variances ranefTable <- stats_mod_max_score$tidy %>% dplyr::filter(effect == "ran_pars") %>% dplyr::select(group, estimate, conf.low, conf.high) %>% as.data.frame() %>% mutate(stat = "rand") %>% rename(term = group) %>% mutate(estimate = estimate^2, conf.high = conf.high^2, conf.low = conf.low^2) # Adjusted repeatabilities coefRptTable <- stats_mod_max_score$rptR$R_boot %>% dplyr::select(-Fixed) %>% mutate(residual = 1 - rowSums(.)) %>% apply(., 2, function(x) c(mean (x), quantile (x, prob = c(0.025, 0.975)))) %>% t() %>% as.data.frame() %>% rownames_to_column("term") %>% rename(estimate = V1, conf.low = `2.5%`, conf.high = `97.5%`) %>% mutate(stat = "RptR") # Store all parameters into a single table and clean it up allCoefs_mod <- bind_rows(fixef_bw_Table, R2Table, ranefTable, coefRptTable, sample_sizes) %>% bind_cols(., mod_comp_names) %>% mutate(coefString = ifelse(!is.na(conf.low), paste0("[", round(conf.low, 2), ", ", round(conf.high, 2), "]"), NA), effect = c(rep("Fixed effects \U1D6FD (standardized)", nrow(fixef_bw_Table)), rep("Partitioned \U1D479\U00B2", nrow(R2Table)), rep("Random effects \U1D70E\U00B2", nrow(ranefTable)), rep("Adjusted repeatability \U1D45F", nrow(coefRptTable)), rep("Sample sizes \U1D45B", nrow(sample_sizes)))) %>% dplyr::select(effect, everything()) # draw gt table mod_max_score_table <- allCoefs_mod %>% dplyr::select(effect, comp_name, estimate, coefString) %>% gt(rowname_col = "row", groupname_col = "effect") %>% cols_label(comp_name = html("Banded Dotterel rufous band score"), estimate = "Mean estimate", coefString = "95% confidence interval") %>% fmt_number(columns = c(estimate), rows = 1:10, decimals = 2, use_seps = FALSE) %>% fmt_number(columns = c(estimate), rows = 11:13, decimals = 0, use_seps = FALSE) %>% sub_missing(columns = 1:4, missing_text = "") %>% cols_align(align = "left", columns = c(comp_name)) %>% tab_options(row_group.font.weight = "bold", row_group.background.color = brewer.pal(9,"Greys")[3], table.font.size = 12, data_row.padding = 3, row_group.padding = 4, summary_row.padding = 2, column_labels.font.size = 14, row_group.font.size = 12, table.width = pct(100)) ``` Males tend to have a more full and immaculate rufous bands than females, however there is substantial overlap in their distributions. #### sex-specific breeding plumage distributions ```{r, echo=FALSE, fig.width=6, fig.align='center'} ind_breeding_scores %>% group_by(rings_comb, sex) %>% summarise(max_breeding_score = max(max_breeding_score)) %>% ungroup() %>% group_by(sex, max_breeding_score) %>% summarise(n = n()) %>% bind_rows(., data.frame(sex = c("F", "M"), max_breeding_score = c(7, 3), n = c(0, 0))) %>% ungroup() %>% ggplot() + geom_bar(aes(x = max_breeding_score, y = n, fill = sex), alpha = 0.5, stat = "identity", position = "dodge", width = 0.5) + theme(text = element_text(family = "Verdana"), legend.title = element_blank(), axis.ticks = element_blank(), axis.text = element_text(size = 10, colour = "grey40"), axis.title.y = element_text(size = 11, colour = "grey40"), axis.text.y = element_text(size = 10, colour = "grey40"), plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"), panel.spacing = unit(1, "cm"), panel.background = element_rect(fill = 'transparent', color = NA), plot.background = element_rect(fill = 'transparent', color = NA), strip.background = element_rect(fill = 'grey90', color = NA), strip.text = element_text(size = 13, colour = "grey40"), panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"), panel.grid.minor = element_blank(), panel.grid.major.x = element_blank()) + theme(legend.position = c(0.15, 0.8)) + xlab("maximum individual rufous band score\nobserved during breeding season") + ylab("Frequency") + scale_colour_brewer(palette = "Dark2", direction = -1, name = "sex", labels = c("Female (N = 49)", "Male (N = 35)")) + scale_fill_brewer(palette = "Dark2", direction = -1, name = "sex", labels = c("Female (N = 49)", "Male (N = 35)")) ``` #### effect-size table for sex-specific breeding plumage model ```{r, echo=FALSE} mod_max_score_table ``` ```{r, include=FALSE} effects_mod_max_score <- as.data.frame(allEffects(mod_max_score)$sex) %>% mutate(x_mean = ifelse(sex == "F", as.numeric(factor(sex)) + 0.2, ifelse(sex == "M", as.numeric(factor(sex)) - 0.2, NA)), x_data = ifelse(sex == "F", as.numeric(factor(sex)), ifelse(sex == "M", as.numeric(factor(sex)), NA))) ``` #### Sex-specific breeding plumage model predictions ```{r, echo=FALSE, fig.width=4, fig.align='center'} ggplot() + geom_errorbar(data = effects_mod_max_score, aes(x = x_mean, ymin = lower, ymax = upper), width = 0.05) + geom_point(data = effects_mod_max_score, aes(x = x_mean, y = fit, fill = sex), shape = 21, size = 5) + geom_jitter(data = ind_breeding_scores, aes(x = as.numeric(factor(sex)), y = max_breeding_score, fill = sex), width = 0.05, height = 0.1, shape = 21, size = 3, alpha = 0.75) + theme(legend.position = "none", text = element_text(family = "Verdana"), axis.ticks = element_blank(), axis.text = element_text(size = 10, colour = "grey40"), axis.title.y = element_text(size = 11, colour = "grey40"), axis.text.y = element_text(size = 10, colour = "grey40"), plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"), panel.spacing = unit(1, "cm"), panel.background = element_rect(fill = 'transparent', color = NA), plot.background = element_rect(fill = 'transparent', color = NA), strip.background = element_rect(fill = 'grey90', color = NA), strip.text = element_text(size = 13, colour = "grey40"), panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"), panel.grid.minor = element_blank(), panel.grid.major.x = element_blank(), axis.title.x = element_blank()) + scale_x_continuous(limits = c(0.8, 2.2), breaks = c(1.1, 1.9), labels = c("female", "male")) + ylab("maximum breeding plumage score observed\n(mean ± 95% CI)") + scale_fill_brewer(palette = "Set2", direction = -1) + scale_y_continuous(limits = c(3.8, 7.2)) ``` ### Age-and sex-specific variation in breeding plumage data wrangle for sex-age model. Standardize max_breeding_score according to sex to make males and females to make them comparable across ages (i.e., does max_breeding_score change across age while accounting for underlying sex differences?) Only applied to known-aged birds. ```{r} # extract the core breeding months (i.e., when presumably all birds are at their maximum breeding plumage), and determine the maximum score for each individual. ind_breeding_scores_age <- dat %>% mutate(score = as.numeric(score)) %>% mutate(breeding_season = ifelse(month(date) %in% c(8, 9, 10, 11, 12), 1, 0)) %>% filter(breeding_season == 1) %>% group_by(season, rings_comb, sex, age) %>% summarise(max_breeding_score = max(score)) %>% ungroup() %>% group_by(sex) %>% mutate(std_max_breeding_score = (max_breeding_score - mean(max_breeding_score, na.rm = TRUE)) / sd(max_breeding_score, na.rm = TRUE)) %>% filter(!is.na(age)) ``` Assess sample sizes of each sex for individuals with known age ```{r} ind_breeding_scores_age %>% group_by(sex) %>% summarise(n_distinct(rings_comb)) ``` mixed model of standardized max breeding score (by sex) with individual as random intercept and age as fixed effect (Note: we tried to include season as an additional random intercept, but the low sample size of repeated measures across seasons caused convergence issues of the model) ```{r} # linear mixed model for the difference in max score between sexes across age mod_max_score_age <- lmer(std_max_breeding_score ~ age + (1 | rings_comb), data = ind_breeding_scores_age) ``` ```{r, eval=FALSE, include=FALSE} # quick look at effect plot(allEffects(mod_max_score_age), type = "response") tbl_regression(mod_max_score_age, intercept = TRUE, label = list(age ~ "Age")) # Derive confidence intervals of effect sizes from parametric bootstrapping tidy_mod_max_score_age <- tidy(mod_max_score_age, conf.int = TRUE, conf.method = "boot", nsim = 1000) # run rptR to obtain repeatabilities of random effects rpt_mod_max_score_age <- rpt(std_max_breeding_score ~ age + (1 | rings_comb), grname = c("rings_comb", "Fixed"), data = ind_breeding_scores_age, datatype = "Gaussian", nboot = 1000, npermut = 1000, ratio = TRUE, adjusted = TRUE, ncores = 4, parallel = TRUE) # run partR2 on each model to obtain marginal R2, parameter estimates, and beta # weights R2m_mod_max_score_age <- partR2(mod_max_score_age, partvars = c("age"), R2_type = "marginal", nboot = 1000, CI = 0.95, max_level = 1) R2c_mod_max_score_age <- partR2(mod_max_score_age, partvars = c("age"), R2_type = "conditional", nboot = 1000, CI = 0.95, max_level = 1) stats_mod_max_score_age <- list(mod = mod_max_score_age, tidy = tidy_mod_max_score_age, rptR = rpt_mod_max_score_age, partR2m = R2m_mod_max_score_age, partR2c = R2c_mod_max_score_age, data = ind_breeding_scores_age) ``` ```{r, include=FALSE} # saveRDS(stats_mod_max_score_age, file = "out/stats_mod_max_score_age.rds") stats_mod_max_score_age <- readRDS(file = "/Users/leberhart/ownCloud/kemp_projects/bdot/moult/out/stats_mod_max_score_age.rds") ``` ```{r, include=FALSE} #### Table of effect sizes ---- # Retrieve sample sizes sample_sizes <- ind_breeding_scores_age %>% ungroup() %>% summarise(Year = n_distinct(season), Individual = n_distinct(rings_comb), Observations = nrow(.)) sample_sizes <- as.data.frame(t(as.data.frame(sample_sizes))) %>% rownames_to_column("term") %>% rename(estimate = V1) %>% mutate(stat = "n") # clean model component names mod_comp_names <- data.frame(comp_name = c("Age", "Total Marginal \U1D479\U00B2", "Age", "Total Conditional \U1D479\U00B2", "Individual", "Residual", "Individual", "Residual", "Years", "Individuals", "Observations")) # Fixed effect sizes (non-standardized) fixefTable <- stats_mod_max_score_age$tidy %>% dplyr::filter(effect == "fixed") %>% dplyr::select(term, estimate, conf.low, conf.high) %>% as.data.frame() %>% mutate(stat = "fixed") # Fixed effect sizes (standardized) fixef_bw_Table <- stats_mod_max_score_age$partR2m$BW %>% as.data.frame() %>% mutate(stat = "fixed_bw") %>% rename(conf.low = CI_lower, conf.high = CI_upper) # Semi-partial R2 estimates R2Table <- bind_rows(stats_mod_max_score_age$partR2m$R2, stats_mod_max_score_age$partR2c$R2[1,]) %>% dplyr::select(term, estimate, CI_lower, CI_upper) %>% as.data.frame() %>% mutate(stat = "partR2") %>% rename(conf.low = CI_lower, conf.high = CI_upper) # Random effects variances ranefTable <- stats_mod_max_score_age$tidy %>% dplyr::filter(effect == "ran_pars") %>% dplyr::select(group, estimate, conf.low, conf.high) %>% as.data.frame() %>% mutate(stat = "rand") %>% rename(term = group) %>% mutate(estimate = estimate^2, conf.high = conf.high^2, conf.low = conf.low^2) # Adjusted repeatabilities coefRptTable <- stats_mod_max_score_age$rptR$R_boot %>% dplyr::select(-Fixed) %>% mutate(residual = 1 - rowSums(.)) %>% apply(., 2, function(x) c(mean (x), quantile (x, prob = c(0.025, 0.975)))) %>% t() %>% as.data.frame() %>% rownames_to_column("term") %>% rename(estimate = V1, conf.low = `2.5%`, conf.high = `97.5%`) %>% mutate(stat = "RptR") # Store all parameters into a single table and clean it up allCoefs_mod <- bind_rows(fixef_bw_Table, R2Table, ranefTable, coefRptTable, sample_sizes) %>% bind_cols(., mod_comp_names) %>% mutate(coefString = ifelse(!is.na(conf.low), paste0("[", round(conf.low, 2), ", ", round(conf.high, 2), "]"), NA), effect = c(rep("Fixed effects \U1D6FD (standardized)", nrow(fixef_bw_Table)), rep("Partitioned \U1D479\U00B2", nrow(R2Table)), rep("Random effects \U1D70E\U00B2", nrow(ranefTable)), rep("Adjusted repeatability \U1D45F", nrow(coefRptTable)), rep("Sample sizes \U1D45B", nrow(sample_sizes)))) %>% dplyr::select(effect, everything()) # draw gt table mod_max_score_age_table <- allCoefs_mod %>% dplyr::select(effect, comp_name, estimate, coefString) %>% gt(rowname_col = "row", groupname_col = "effect") %>% cols_label(comp_name = html("Banded Dotterel rufous band score"), estimate = "Mean estimate", coefString = "95% confidence interval") %>% fmt_number(columns = c(estimate), rows = 1:10, decimals = 2, use_seps = FALSE) %>% fmt_number(columns = c(estimate), rows = 9:11, decimals = 0, use_seps = FALSE) %>% sub_missing(columns = 1:4, missing_text = "") %>% cols_align(align = "left", columns = c(comp_name)) %>% tab_options(row_group.font.weight = "bold", row_group.background.color = brewer.pal(9,"Greys")[3], table.font.size = 12, data_row.padding = 3, row_group.padding = 4, summary_row.padding = 2, column_labels.font.size = 14, row_group.font.size = 12, table.width = pct(100)) ``` #### effect-size table for sex- and age-specific variation in breeding plumage no effect of age on the maximum extent of an individual's rufous band score while controlling for underlying sex-specific variation ```{r, echo=FALSE} mod_max_score_age_table ``` #### age-specific breeding plumage model predictions ```{r, echo=FALSE} # extract predicted trends pr <- ggeffects::predict_response(mod_max_score_age) # join the back-transformed dates to model fits mod_max_score_age_fits <- as.data.frame(pr) ggplot() + geom_ribbon(data = mod_max_score_age_fits, aes(x = age.x, ymin = age.conf.low, ymax = age.conf.high), fill = "grey70", alpha = 0.5) + geom_line(data = mod_max_score_age_fits, aes(x = age.x, y = age.predicted), color = "grey40") + geom_jitter(data = ind_breeding_scores_age, aes(x = age, y = std_max_breeding_score, fill = sex), width = 0.05, height = 0.1, shape = 21, size = 3, alpha = 0.75, alpha = 1) + theme(legend.position = c(0.15, 0.2), legend.title = element_blank(), text = element_text(family = "Verdana"), axis.ticks = element_blank(), axis.text = element_text(size = 10, colour = "grey40"), axis.title.y = element_text(size = 11, colour = "grey40"), axis.text.y = element_text(size = 10, colour = "grey40"), plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"), panel.spacing = unit(1, "cm"), panel.background = element_rect(fill = 'transparent', color = NA), plot.background = element_rect(fill = 'transparent', color = NA), strip.background = element_rect(fill = 'grey90', color = NA), strip.text = element_text(size = 13, colour = "grey40"), panel.grid.major = element_line(size = 0.25, lineend = "round", colour = "grey90"), panel.grid.minor = element_blank()) + scale_x_continuous(limits = c(0.5, 6.5), breaks = c(1:6), labels = c(1:6)) + ylab("sex-standardized\nmaximum breeding plumage score observed") + xlab("age") + scale_fill_brewer(palette = "Dark2", direction = -1, name = "sex", labels = c("Female (N = 14)", "Male (N = 10)")) ``` ### Migratory status-and sex-specific variation in breeding plumage data wrangle for sex-migratory status model - only applied to birds with known migratory status based on winter resightings ```{r} # extract the core breeding months (i.e., when presumably all birds are at their maximum breeding plumage), and determine the maximum score for each individual. ind_breeding_scores_status <- dat %>% mutate(score = as.numeric(score)) %>% mutate(breeding_season = ifelse(month(date) %in% c(8, 9, 10, 11, 12), 1, 0)) %>% filter(breeding_season == 1) %>% group_by(season, rings_comb, sex, age, migratory_status) %>% summarise(max_breeding_score = max(score)) %>% ungroup() %>% group_by(sex) %>% mutate(std_max_breeding_score = (max_breeding_score - mean(max_breeding_score, na.rm = TRUE)) / sd(max_breeding_score, na.rm = TRUE)) %>% filter(migratory_status %in% c("M", "R")) ``` Assess sample sizes of each sex and migratory status (for birds with known status) ```{r, echo=TRUE} # Assess sample sizes of each sex and season # (13 females (2 migrant, 11 resident), 8 males (3 migrant, 5 resident)) ind_breeding_scores_status %>% group_by(sex, migratory_status) %>% summarise(n_distinct(rings_comb)) ``` mixed model of standardized max breeding score (by sex) with individual as random intercept and migratory status as fixed effect (Note: we tried to include season as an additional random intercept, but the low sample size of repeated measures across seasons caused convergence issues of the model) ```{r} # linear mixed model for the difference in max score between sexes mod_max_score_status <- lmer(std_max_breeding_score ~ migratory_status + (1 | rings_comb), data = ind_breeding_scores_status) ``` ```{r, eval=FALSE, include=FALSE} # # detected singularity issue in a model with (1 | season), summary showed # that variance component of this random effect was zero. # # Model with one random effect mod1 <- lmer(std_max_breeding_score ~ migratory_status + season + (1 | rings_comb), data = ind_breeding_scores_status) mod2 <- lmer(std_max_breeding_score ~ migratory_status + (1 | season), data = ind_breeding_scores_status) mod3 <- lmer(std_max_breeding_score ~ migratory_status + (1 | rings_comb), data = ind_breeding_scores_status) # Compare with the full model mod_full <- lmer(std_max_breeding_score ~ migratory_status + (1 | rings_comb) + (1 | season), data = ind_breeding_scores_status) # Check the summaries summary(mod1) summary(mod2) summary(mod3) summary(mod_full) # Compare models using AIC AIC(mod1, mod2, mod3, mod_full) # conclude that the term (1 | season) is not needed for statistical reasons, but also since we are not interested in population-level annual variation in the max-breeding plumage scores we can drop this ``` ```{r, eval=FALSE, include=FALSE} # quick look at effect plot(allEffects(mod_max_score_status), type = "response") tbl_regression(mod_max_score_status, intercept = TRUE, label = list(migratory_status ~ "migratory status")) # Derive confidence intervals of effect sizes from parametric bootstrapping tidy_mod_max_score_status <- tidy(mod_max_score_status, conf.int = TRUE, conf.method = "boot", nsim = 1000) # run rptR to obtain repeatabilities of random effects rpt_mod_max_score_status <- rpt(std_max_breeding_score ~ migratory_status + (1 | rings_comb), grname = c("rings_comb", "Fixed"), data = ind_breeding_scores_status, datatype = "Gaussian", nboot = 1000, npermut = 1000, ratio = TRUE, adjusted = TRUE, ncores = 4, parallel = TRUE) # run partR2 on each model to obtain marginal R2, parameter estimates, and beta # weights R2m_mod_max_score_status <- partR2(mod_max_score_status, partvars = c("migratory_status"), R2_type = "marginal", nboot = 1000, CI = 0.95, max_level = 1) R2c_mod_max_score_status <- partR2(mod_max_score_status, partvars = c("migratory_status"), R2_type = "conditional", nboot = 1000, CI = 0.95, max_level = 1) stats_mod_max_score_status <- list(mod = mod_max_score_status, tidy = tidy_mod_max_score_status, rptR = rpt_mod_max_score_status, partR2m = R2m_mod_max_score_status, partR2c = R2c_mod_max_score_status, data = ind_breeding_scores_status) ``` ```{r, include=FALSE} # saveRDS(stats_mod_max_score_status, file = "out/stats_mod_max_score_status.rds") stats_mod_max_score_status <- readRDS(file = "/Users/leberhart/ownCloud/kemp_projects/bdot/moult/out/stats_mod_max_score_status.rds") ``` ```{r, echo=FALSE} #### Table of effect sizes ---- # Retrieve sample sizes sample_sizes <- ind_breeding_scores_status %>% ungroup() %>% summarise(Year = n_distinct(season), Individual = n_distinct(rings_comb), Observations = nrow(.)) sample_sizes <- as.data.frame(t(as.data.frame(sample_sizes))) %>% rownames_to_column("term") %>% rename(estimate = V1) %>% mutate(stat = "n") # clean model component names mod_comp_names <- data.frame(comp_name = c("Resident (Migratory Status)", "Total Marginal \U1D479\U00B2", "Migratory Status", "Total Conditional \U1D479\U00B2", "Individual", "Residual", "Individual", "Residual", "Years", "Individuals", "Observations")) # Fixed effect sizes (non-standardized) fixefTable <- stats_mod_max_score_status$tidy %>% dplyr::filter(effect == "fixed") %>% dplyr::select(term, estimate, conf.low, conf.high) %>% as.data.frame() %>% mutate(stat = "fixed") # Fixed effect sizes (standardized) fixef_bw_Table <- stats_mod_max_score_status$partR2m$BW %>% as.data.frame() %>% mutate(stat = "fixed_bw") %>% rename(conf.low = CI_lower, conf.high = CI_upper) # Semi-partial R2 estimates R2Table <- bind_rows(stats_mod_max_score_status$partR2m$R2, stats_mod_max_score_status$partR2c$R2[1,]) %>% dplyr::select(term, estimate, CI_lower, CI_upper) %>% as.data.frame() %>% mutate(stat = "partR2") %>% rename(conf.low = CI_lower, conf.high = CI_upper) # Random effects variances ranefTable <- stats_mod_max_score_status$tidy %>% dplyr::filter(effect == "ran_pars") %>% dplyr::select(group, estimate, conf.low, conf.high) %>% as.data.frame() %>% mutate(stat = "rand") %>% rename(term = group) %>% mutate(estimate = estimate^2, conf.high = conf.high^2, conf.low = conf.low^2) # Adjusted repeatabilities coefRptTable <- stats_mod_max_score_status$rptR$R_boot %>% dplyr::select(-Fixed) %>% mutate(residual = 1 - rowSums(.)) %>% apply(., 2, function(x) c(mean (x), quantile (x, prob = c(0.025, 0.975)))) %>% t() %>% as.data.frame() %>% rownames_to_column("term") %>% rename(estimate = V1, conf.low = `2.5%`, conf.high = `97.5%`) %>% mutate(stat = "RptR") # Store all parameters into a single table and clean it up allCoefs_mod <- bind_rows(fixef_bw_Table, R2Table, ranefTable, coefRptTable, sample_sizes) %>% bind_cols(., mod_comp_names) %>% mutate(coefString = ifelse(!is.na(conf.low), paste0("[", round(conf.low, 2), ", ", round(conf.high, 2), "]"), NA), effect = c(rep("Fixed effects \U1D6FD (standardized)", nrow(fixef_bw_Table)), rep("Partitioned \U1D479\U00B2", nrow(R2Table)), rep("Random effects \U1D70E\U00B2", nrow(ranefTable)), rep("Adjusted repeatability \U1D45F", nrow(coefRptTable)), rep("Sample sizes \U1D45B", nrow(sample_sizes)))) %>% dplyr::select(effect, everything()) mod_max_score_table_status <- allCoefs_mod %>% dplyr::select(effect, comp_name, estimate, coefString) %>% gt(rowname_col = "row", groupname_col = "effect") %>% cols_label(comp_name = html("Banded Dotterel rufous band score"), estimate = "Mean estimate", coefString = "95% confidence interval") %>% fmt_number(columns = c(estimate), rows = 1:8, decimals = 2, use_seps = FALSE) %>% fmt_number(columns = c(estimate), rows = 9:11, decimals = 0, use_seps = FALSE) %>% sub_missing(columns = 1:4, missing_text = "") %>% cols_align(align = "left", columns = c(comp_name)) %>% tab_options(row_group.font.weight = "bold", row_group.background.color = brewer.pal(9,"Greys")[3], table.font.size = 12, data_row.padding = 3, row_group.padding = 4, summary_row.padding = 2, column_labels.font.size = 14, row_group.font.size = 12, table.width = pct(80)) ``` #### effect-size table and plot for sex- and migratory-status breeding plumage no effect of migratory status on an individual's rufous band score while controlling for underlying sex-specific variation ```{r, echo=FALSE} mod_max_score_table_status ``` #### migratory status-specific breeding plumage model predictions ```{r, echo=FALSE, fig.width=6, fig.align='center'} effects_mod_max_score_status2 <- as.data.frame(allEffects(mod_max_score_status)$migratory_status) %>% mutate(x_mean = ifelse(migratory_status == "M", as.numeric(factor(migratory_status)) + 0.2, ifelse(migratory_status == "R", as.numeric(factor(migratory_status)) - 0.2, NA)), x_data = ifelse(migratory_status == "M", as.numeric(factor(migratory_status)), ifelse(migratory_status == "R", as.numeric(factor(migratory_status)), NA))) ggplot() + geom_errorbar(data = effects_mod_max_score_status2, aes(x = x_mean, ymin = lower, ymax = upper), width = 0.05) + geom_point(data = effects_mod_max_score_status2, aes(x = x_mean, y = fit, fill = migratory_status), shape = 21, size = 5) + geom_jitter(data = ind_breeding_scores_status, aes(x = as.numeric(factor(migratory_status)), y = std_max_breeding_score, fill = migratory_status), width = 0.05, height = 0.1, shape = 21, size = 3, alpha = 0.75) + theme(legend.position = "none", text = element_text(family = "Verdana"), axis.ticks = element_blank(), axis.text = element_text(size = 10, colour = "grey40"), axis.title.y = element_text(size = 11, colour = "grey40"), plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"), panel.spacing = unit(1, "cm"), panel.background = element_rect(fill = 'transparent', color = NA), plot.background = element_rect(fill = 'transparent', color = NA), panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"), panel.grid.minor = element_blank(), panel.grid.major.x = element_blank(), axis.title.x = element_blank()) + scale_x_continuous(limits = c(0.8, 2.2), breaks = c(1.1, 1.9), labels = c("migrant", "resident")) + ylab("sex-standardized\nmaximum breeding plumage score observed") + scale_fill_brewer(palette = "Set1") ``` ## Sources of individual variation in the timing of pre-basic moult ### Within-individual moult dynamics across sexes wrangle data by calculating the individual proportional moult scores by comparing each score to a given individual's max (determined in previous chunk). Piece apart sex-specific variation in the timing of pre-basic moult ```{r, include=FALSE} ind_prop_molt_scores <- dat %>% mutate(score = as.numeric(score)) %>% left_join(., select(ind_breeding_scores, -sex), by = c("rings_comb","season")) %>% filter(!is.na(max_breeding_score)) %>% filter(date_J_of_max_score <= date_J) %>% group_by(sex) %>% mutate(std_max_breeding_score = (max_breeding_score - mean(max_breeding_score, na.rm = TRUE)) / sd(max_breeding_score, na.rm = TRUE)) %>% # focus on key months of moult (Nov, Dec, Jan, Feb, March) filter(month(date) %in% c(11, 12, 1, 2, 3)) %>% # subtract 1 to make minimum score 0 mutate(prop_molt_score_max = (score-1)/(max_breeding_score-1), prop_molt_score_mode = (score-1)/(mode_breeding_score-1), prop_molt_score_max_mean = (score-1)/(max_breeding_score_mean-1)) %>% select(rings_comb, season, date_J, sex, migratory_status, age, score, max_breeding_score, mode_breeding_score, max_breeding_score_mean, std_max_breeding_score, prop_molt_score_max, prop_molt_score_mode, prop_molt_score_max_mean) %>% arrange(rings_comb, season, date_J) %>% distinct() %>% filter(!is.na(prop_molt_score_max)) %>% ungroup() %>% mutate(prop_molt_score_max_mean = ifelse(prop_molt_score_max_mean > 1, 1, prop_molt_score_max_mean), prop_molt_score_mode = ifelse(prop_molt_score_mode > 1, 1, prop_molt_score_mode)) %>% mutate(ring_season = paste(rings_comb, season, sep = "_")) ``` Assess sample sizes of each sex ```{r, echo=FALSE} ind_prop_molt_scores %>% group_by(sex) %>% summarise(n_distinct(rings_comb)) ``` mixed effects binomial model assessing sex-differences in the timing of pre-basic moult while accounting for bird- and year-specific random variation in the timing of pre-basic moult (i.e., random slope model of date for individual and year). ```{r} mod_moult_sex_bird_RIS <- lme4::glmer(prop_molt_score_max ~ date_J + sex + (date_J | ring_season), data = ind_prop_molt_scores, family = binomial) ``` ```{r, echo=FALSE} tbl_regression(mod_moult_sex_bird_RIS, intercept = TRUE, label = list(date_J ~ "Date", sex ~ "Sex")) ``` ```{r, echo=FALSE} RIS_bird_sex <- augment(mod_moult_sex_bird_RIS) %>% dplyr::select(prop_molt_score_max, date_J, ring_season, .fitted, sex) dates_for_plot <- data.frame(date = as.Date(c(as.Date("2021-07-01"):as.Date("2022-06-30"))), date_J = c(1:365)) df_bird_sex <- RIS_bird_sex %>% dplyr::group_by(sex, ring_season, date_J) %>% dplyr::summarise(RIS = mean(.fitted)) %>% mutate(RIS_trans = exp(RIS)/(1 + exp(RIS))) %>% left_join(., dates_for_plot, by = "date_J") %>% mutate(rings_comb = as.factor(ring_season)) %>% filter(date > as.Date("2021-11-01")) # extract predicted trends pr <- ggeffects::predict_response(mod_moult_sex_bird_RIS, c("date_J [30:293]", "sex")) # join the back-transformed dates to model fits mod_moult_sex_bird_RIS_fits <- as.data.frame(pr) %>% rename(date_J = x, sex = group) %>% left_join(., dates_for_plot, by = "date_J") %>% filter(date > as.Date("2021-11-01")) plot_rs_bird_sex <- ggplot() + geom_line(data = df_bird_sex, aes(x = date, y = RIS_trans, group = ring_season), alpha = 0.25, colour = "grey50") + # geom_line(data = df_bird_season %>% filter(rings_comb == "RRGO"), #"RBRL"), # aes(x = date, y = RIS_trans, # group = bird_season), # alpha = 1, colour = "black") + # geom_jitter(data = mid_point_moult_df, # aes(x = date, y = 0.75), # height = 0.025, color = "red") + # geom_jitter(data = moult_commence_ring_season, # aes(x = date, y = 0.25), # height = 0.025, color = "blue") + # geom_point(data = mid_point_moult_df %>% filter(rings_comb == "RRGO"), # aes(x = date, y = 0.55)) + # geom_point(data = moult_commence_ring_season %>% filter(rings_comb == "RRGO"), # aes(x = date, y = 0.45)) + theme(legend.position = "none", legend.justification = c(1, 0), legend.title = element_blank(), text = element_text(family = "Verdana"), axis.ticks = element_blank(), axis.text.x = element_text(size = 10, angle = 45, hjust = 1, vjust = 1), axis.text.y = element_text(size = 10, colour = "grey40"), axis.title.y = element_text(size = 11, colour = "grey40"), plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"), panel.spacing = unit(1, "cm"), panel.background = element_rect(fill = 'transparent', color = NA), plot.background = element_rect(fill = 'transparent', color = NA), panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"), panel.grid.minor = element_blank(), panel.grid.major.x = element_line(size = 0.25, lineend = "round", colour = "grey90"), axis.title.x = element_blank()) + scale_y_continuous(limits = c(-0.05, 1.05)) + ylab("proportion intact of individual-based maximum rufous band") + scale_x_date(date_labels = "%B", expand = c(0.01, 0.01), date_breaks = "1 month") + ggtitle("random slopes of\npre-basic moult timing") # plot the model plot_rs_bird_fs_sex <- ggplot() + geom_line(data = mod_moult_sex_bird_RIS_fits, aes(x = date, y = predicted, color = sex)) + geom_ribbon(data = mod_moult_sex_bird_RIS_fits, aes(x = date, ymax = conf.high, ymin = conf.low, fill = sex), lwd = 1, alpha = 0.25) + geom_jitter(data = ind_prop_molt_scores %>% left_join(., dates_for_plot, by = "date_J"), aes(x = date, y = prop_molt_score_max, color = sex), alpha = 0.25, shape = 20, width = 0, height = 0.025) + theme(legend.position = c(0.55, 0.1), # legend.background = element_rect(fill = 'transparent', color = NA), legend.justification = c(1, 0), legend.title = element_blank(), text = element_text(family = "Verdana"), axis.ticks = element_blank(), axis.text.x = element_text(size = 10, angle = 45, hjust = 1, vjust = 1, colour = "grey40"), axis.text.y = element_blank(), axis.title.y = element_blank(), plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"), panel.spacing = unit(1, "cm"), panel.background = element_rect(fill = 'transparent', color = NA), plot.background = element_rect(fill = 'transparent', color = NA), panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"), panel.grid.minor = element_blank(), panel.grid.major.x = element_line(size = 0.25, lineend = "round", colour = "grey90"), axis.title.x = element_blank()) + scale_y_continuous(limits = c(-0.05, 1.05)) + ylab("proportion intact of individual-based maximum rufous band") + scale_x_date(date_labels = "%B", expand = c(0.01, 0.01), date_breaks = "1 month") + scale_colour_brewer(palette = "Dark2", direction = -1, name = "Sex", labels = c("Female (N = 49)", "Male (N = 32)")) + scale_fill_brewer(palette = "Dark2", direction = -1, name = "Sex", labels = c("Female (N = 49)", "Male (N = 32)")) + ggtitle("sex-specific slopes\nof pre-basic moult timing") plot_rs_bird_sex + plot_rs_bird_fs_sex ``` ```{r, include=FALSE, eval=FALSE} # # quick look at effect # plot(allEffects(mod_moult_sex_bird_season_RIS), type = "response") # tbl_regression(mod_moult_sex_bird_season_RIS) # # # not much variation in slopes across seasons, so simplify the model to only include the (date_J | rings_comb) term. # mod_moult_sex_bird_RIS <- # lme4::glmer(prop_molt_score ~ # date_J + sex + # (date_J | rings_comb), # data = ind_prop_molt_scores, # family = binomial) # # tab_model(mod_moult_sex_bird_RIS) # # # Load the package # library(performance) # # # Calculate R-squared # r2(mod_moult_sex_bird_RIS) # # # Load the package # library(MuMIn) # # # Calculate R-squared # r.squaredGLMM(mod_moult_sex_bird_RIS) # # # Derive confidence intervals of effect sizes from parametric bootstrapping # tidy_mod_moult_sex_bird_RIS <- # tidy(mod_moult_sex_bird_RIS, conf.int = TRUE, conf.method = "boot", nsim = 1000) # # saveRDS(tidy_mod_moult_sex_bird_RIS, here("out/tidy_mod_moult_sex_bird_RIS.rds")) ``` ```{r, include=FALSE} # derive the mid-point date of each individual's moult # wrangle the dates of plumage retention and moult for subsequent analysis # Step 1: Calculate the max_score for each individual and season max_scores <- ind_prop_molt_scores %>% group_by(rings_comb, season) %>% summarise(max_score = max(prop_molt_score_max, na.rm = TRUE), .groups = "drop") # Step 2: Merge the max_scores back into the original data mid_point_dates <- ind_prop_molt_scores %>% left_join(max_scores, by = c("rings_comb", "season")) %>% # Add the max_score group_by(rings_comb, season) %>% arrange(date_J) %>% # Ensure data is sorted by date_J # Step 3: Find the latest date_J for the maximum prop_molt_score filter(prop_molt_score_max == max_score) %>% slice_max(order_by = date_J, n = 1, with_ties = FALSE) %>% rename(latest_max_date_J = date_J) %>% # Step 4: Find the earliest date_J where prop_molt_score < max_score right_join( ind_prop_molt_scores %>% left_join(max_scores, by = c("rings_comb", "season")) %>% # Add the max_score here too group_by(rings_comb, season) %>% arrange(date_J) %>% filter(prop_molt_score_max < max_score) %>% slice_min(order_by = date_J, n = 1, with_ties = FALSE) %>% rename(earliest_decline_date_J = date_J), by = c("rings_comb", "season") ) %>% # Step 5: Compute the mid-point date_J mutate(mid_point_date_J = round((latest_max_date_J + earliest_decline_date_J) / 2)) %>% # Select relevant columns select(rings_comb, season, latest_max_date_J, earliest_decline_date_J, mid_point_date_J) %>% distinct() dates_for_plot <- data.frame(date = as.Date(c(as.Date("2021-07-01"):as.Date("2022-06-30"))), date_J = c(1:365)) moult_commence_ring_season <- left_join(mid_point_dates, dates_for_plot %>% rename(mid_point_date_J = date_J), by = "mid_point_date_J") %>% mutate(season = as.factor(season)) ``` ### Within-individual moult dynamics across migratory status ```{r, include=FALSE} # calculate the individual proportional moult scores by comparing each score to a given individual's max (determined in previous chunk). Piece apart variation in migratory status. ind_prop_molt_scores_status <- ind_prop_molt_scores %>% filter(migratory_status %in% c("R", "M")) ``` ```{r, echo=FALSE} # Assess sample sizes of each migratory status ind_prop_molt_scores_status %>% group_by(migratory_status) %>% summarise(n_distinct(rings_comb)) ``` ```{r} # mixed effects binomial model comparing migratory status and date effect on the changes in moult scores mod_moult_status_bird_RIS <- lme4::glmer(prop_molt_score_max ~ date_J + migratory_status + (date_J|ring_season), data = ind_prop_molt_scores_status, family = binomial) ``` ```{r, echo=FALSE} tbl_regression(mod_moult_status_bird_RIS, intercept = TRUE, label = list(date_J ~ "Date", migratory_status ~ "Migratory status")) ``` ```{r, echo=FALSE} RIS_bird_status <- augment(mod_moult_status_bird_RIS) %>% dplyr::select(prop_molt_score_max, date_J, ring_season, .fitted, migratory_status) dates_for_plot <- data.frame(date = as.Date(c(as.Date("2021-07-01"):as.Date("2022-06-30"))), date_J = c(1:365)) df_bird_status <- RIS_bird_status %>% dplyr::group_by(migratory_status, ring_season, date_J) %>% dplyr::summarise(RIS = mean(.fitted)) %>% mutate(RIS_trans = exp(RIS)/(1 + exp(RIS))) %>% left_join(., dates_for_plot, by = "date_J") %>% mutate(rings_comb = as.factor(ring_season)) %>% filter(date > as.Date("2021-11-01")) # extract predicted trends pr <- ggeffects::predict_response(mod_moult_status_bird_RIS, c("date_J [30:293]", "migratory_status")) # join the back-transformed dates to model fits mod_moult_status_bird_RIS_fits <- as.data.frame(pr) %>% rename(date_J = x, migratory_status = group) %>% left_join(., dates_for_plot, by = "date_J") %>% filter(date > as.Date("2021-11-01")) plot_rs_bird_status <- ggplot() + geom_line(data = df_bird_status, aes(x = date, y = RIS_trans, group = ring_season), alpha = 0.25, colour = "grey50") + theme(legend.position = "none", legend.justification = c(1, 0), legend.title = element_blank(), text = element_text(family = "Verdana"), axis.ticks = element_blank(), axis.text.x = element_text(size = 10, angle = 45, hjust = 1, vjust = 1), axis.text.y = element_text(size = 10, colour = "grey40"), axis.title.y = element_text(size = 11, colour = "grey40"), plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"), panel.spacing = unit(1, "cm"), panel.background = element_rect(fill = 'transparent', color = NA), plot.background = element_rect(fill = 'transparent', color = NA), panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"), panel.grid.minor = element_blank(), panel.grid.major.x = element_line(size = 0.25, lineend = "round", colour = "grey90"), axis.title.x = element_blank()) + scale_y_continuous(limits = c(-0.05, 1.05)) + ylab("proportion intact of\nindividual-based maximum rufous band") + scale_x_date(date_labels = "%B", expand = c(0.01, 0.01), date_breaks = "1 month") + ggtitle("random slopes of\npre-basic moult timing") # plot the model plot_rs_bird_status_fs <- ggplot() + geom_line(data = mod_moult_status_bird_RIS_fits, aes(x = date, y = predicted, color = migratory_status)) + geom_ribbon(data = mod_moult_status_bird_RIS_fits, aes(x = date, ymax = conf.high, ymin = conf.low, fill = migratory_status), lwd = 1, alpha = 0.25) + geom_jitter(data = ind_prop_molt_scores_status %>% left_join(., dates_for_plot, by = "date_J"), aes(x = date, y = prop_molt_score_max, color = migratory_status), alpha = 0.25, shape = 20, width = 0, height = 0.025) + theme(legend.position = c(0.55, 0.1), # legend.background = element_rect(fill = 'transparent', color = NA), legend.justification = c(1, 0), legend.title = element_blank(), text = element_text(family = "Verdana"), axis.ticks = element_blank(), axis.text.x = element_text(size = 10, angle = 45, hjust = 1, vjust = 1, colour = "grey40"), axis.text.y = element_blank(), axis.title.y = element_blank(), plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"), panel.spacing = unit(1, "cm"), panel.background = element_rect(fill = 'transparent', color = NA), plot.background = element_rect(fill = 'transparent', color = NA), panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"), panel.grid.minor = element_blank(), panel.grid.major.x = element_line(size = 0.25, lineend = "round", colour = "grey90"), axis.title.x = element_blank()) + scale_y_continuous(limits = c(-0.05, 1.05)) + ylab("proportion intact of\nindividual-based maximum rufous band") + scale_x_date(date_labels = "%B", expand = c(0.01, 0.01), date_breaks = "1 month") + scale_colour_brewer(palette = "Set1", labels = c("Migrant (N = 5)", "Resident (N = 16)")) + scale_fill_brewer(palette = "Set1", labels = c("Migrant (N = 5)", "Resident (N = 16)")) + ggtitle("status-specific slopes\nof pre-basic moult timing") plot_rs_bird_status + plot_rs_bird_status_fs ``` ### Within-individual moult dynamics across ages ```{r, include=FALSE} # calculate the individual proportional moult scores by comparing each score to a given individual's max (determined in previous chunk). Piece apart variation between age groups. ind_prop_molt_scores_age <- ind_prop_molt_scores %>% filter(!is.na(age)) ``` ```{r, echo=FALSE} # Assess sample sizes of each sex ind_prop_molt_scores_age %>% group_by(age) %>% summarise(n_distinct(rings_comb)) ``` ```{r} # mixed effects binomial model comparing migratory status and date effect on the changes in moult scores mod_moult_age_bird_RIS <- lme4::glmer(prop_molt_score_max ~ date_J + age + (date_J | ring_season), data = ind_prop_molt_scores_age, family = binomial) ``` ```{r, include=FALSE} # ind_prop_molt_scores_age %>% # mutate(id_year = paste(rings_comb, season, sep = "_")) %>% # group_by(id_year) %>% # summarise(n_ = n()) %>% View() ``` ```{r,echo=FALSE} tbl_regression(mod_moult_age_bird_RIS, intercept = TRUE, label = list(date_J ~ "Date", age ~ "Age")) ``` ```{r, echo=FALSE} RIS_bird_age <- augment(mod_moult_age_bird_RIS) %>% dplyr::select(prop_molt_score_max, date_J, ring_season, .fitted, age) dates_for_plot <- data.frame(date = as.Date(c(as.Date("2021-07-01"):as.Date("2022-06-30"))), date_J = c(1:365)) df_bird_age <- RIS_bird_age %>% dplyr::group_by(age, ring_season, date_J) %>% dplyr::summarise(RIS = mean(.fitted)) %>% mutate(RIS_trans = exp(RIS)/(1 + exp(RIS))) %>% left_join(., dates_for_plot, by = "date_J") %>% mutate(id_year = as.factor(ring_season)) %>% filter(date > as.Date("2021-11-01")) # extract predicted trends pr <- ggeffects::predict_response(mod_moult_age_bird_RIS, c("date_J [30:293]", "age")) # join the back-transformed dates to model fits mod_moult_age_bird_RIS_fits <- as.data.frame(pr) %>% rename(date_J = x, age = group) %>% left_join(., dates_for_plot, by = "date_J") %>% filter(date > as.Date("2021-11-01")) plot_rs_bird_age <- ggplot() + geom_line(data = df_bird_age, aes(x = date, y = RIS_trans, group = ring_season), alpha = 0.25, colour = "grey50") + theme(legend.position = "none", legend.justification = c(1, 0), legend.title = element_blank(), text = element_text(family = "Verdana"), axis.ticks = element_blank(), axis.text.x = element_text(size = 10, angle = 45, hjust = 1, vjust = 1), axis.text.y = element_text(size = 10, colour = "grey40"), axis.title.y = element_text(size = 11, colour = "grey40"), plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"), panel.spacing = unit(1, "cm"), panel.background = element_rect(fill = 'transparent', color = NA), plot.background = element_rect(fill = 'transparent', color = NA), panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"), panel.grid.minor = element_blank(), panel.grid.major.x = element_line(size = 0.25, lineend = "round", colour = "grey90"), axis.title.x = element_blank()) + scale_y_continuous(limits = c(-0.05, 1.05)) + ylab("proportion intact of\nindividual-based maximum rufous band") + scale_x_date(date_labels = "%B", expand = c(0.01, 0.01), date_breaks = "1 month") + ggtitle("random slopes of\npre-basic moult timing") # plot the model plot_rs_bird_age_fs <- ggplot() + geom_line(data = mod_moult_age_bird_RIS_fits %>% mutate(age = as.factor(age)), aes(x = date, y = predicted, color = age)) + geom_ribbon(data = mod_moult_age_bird_RIS_fits %>% mutate(age = as.factor(age)), aes(x = date, ymax = conf.high, ymin = conf.low, fill = age), lwd = 1, alpha = 0.25) + geom_jitter(data = ind_prop_molt_scores_age %>% left_join(., dates_for_plot, by = "date_J") %>% mutate(age = as.factor(age)), aes(x = date, y = prop_molt_score_max, color = age), alpha = 1, shape = 20, width = 0, height = 0.025) + theme(legend.position = c(0.25, 0.1), # legend.background = element_rect(fill = 'transparent', color = NA), legend.justification = c(1, 0), legend.title = element_blank(), text = element_text(family = "Verdana"), axis.ticks = element_blank(), axis.text.x = element_text(size = 10, angle = 45, hjust = 1, vjust = 1, colour = "grey40"), axis.text.y = element_blank(), axis.title.y = element_blank(), plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"), panel.spacing = unit(1, "cm"), panel.background = element_rect(fill = 'transparent', color = NA), plot.background = element_rect(fill = 'transparent', color = NA), panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"), panel.grid.minor = element_blank(), panel.grid.major.x = element_line(size = 0.25, lineend = "round", colour = "grey90"), axis.title.x = element_blank()) + scale_y_continuous(limits = c(-0.05, 1.05)) + ylab("proportion intact of\nindividual-based maximum rufous band") + scale_x_date(date_labels = "%B", expand = c(0.01, 0.01), date_breaks = "1 month") + scale_colour_brewer(palette = "Spectral", direction = -1, name = "Age", labels = c("1 (N = 6)", "2 (N = 5)", "3 (N = 12)", "4 (N = 13", "5 (N = 4)", "6 (N = 2)")) + scale_fill_brewer(palette = "Spectral", direction = -1, name = "Age", labels = c("1 (N = 6)", "2 (N = 5)", "3 (N = 12)", "4 (N = 13", "5 (N = 4)", "6 (N = 2)")) + ggtitle("age-specific slopes of\npre-basic moult timing") plot_rs_bird_age + plot_rs_bird_age_fs ``` inspect the random slopes against the raw data from photos - potential issues with data coverage over the moulting period? ```{r, echo=FALSE, fig.height=20} ggplot() + geom_line(data = df_bird_age %>% mutate(age = as.factor(age)), aes(x = date, y = RIS_trans),#, color = age), alpha = 1) + geom_point(data = ind_prop_molt_scores_age %>% left_join(., dates_for_plot, by = "date_J") %>% mutate(age = as.factor(age), id_year = paste(rings_comb, season, sep = "_")), aes(x = date, y = prop_molt_score_max),# color = age), alpha = 1, shape = 20) + theme(legend.position = "none", legend.justification = c(1, 0), legend.title = element_blank(), text = element_text(family = "Verdana"), axis.ticks = element_blank(), axis.text.x = element_text(size = 10, angle = 45, hjust = 1, vjust = 1), axis.text.y = element_text(size = 10, colour = "grey40"), axis.title.y = element_text(size = 11, colour = "grey40"), plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"), panel.spacing = unit(1, "cm"), panel.background = element_rect(fill = 'transparent', color = NA), plot.background = element_rect(fill = 'transparent', color = NA), panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"), panel.grid.minor = element_blank(), panel.grid.major.x = element_line(size = 0.25, lineend = "round", colour = "grey90"), axis.title.x = element_blank()) + scale_y_continuous(limits = c(-0.05, 1.05)) + ylab("proportion intact of individual-based maximum rufous band") + scale_x_date(date_labels = "%B", expand = c(0.01, 0.01), date_breaks = "1 month") + facet_wrap("id_year", ncol = 3) #+ # scale_colour_brewer(palette = "Spectral", direction = -1) ``` ## Breeding status analysis ```{r, include=FALSE} # explore datasets provided by Ted ## 2021 season # import and consolidate key columns breeding_data_2021 <- read_excel("/Users/leberhart/ownCloud/kemp_projects/bdot/moult/data/Kaikoura_Dotterel_2021_Feb2022_upd_Nov22_Locations.xlsx", sheet = "Kaikoura_NestVisit2021_1", col_types = "text") %>% mutate(visit_date_ = as.POSIXct(as.numeric(`Visit Date`) * 86400, origin = "1899-12-30", tz = "UTC")) %>% mutate(visit_date_nz = with_tz(visit_date_, tzone = "Pacific/Auckland")) %>% mutate(date = as.Date(visit_date_nz)) %>% select(OBJECTID, NestID, date, `Nest Status`, `Egg count`, `Number Hatched`, Notes, `Number Fledged`, `Number of chicks seen`) %>% rename(nest_fate = `Nest Status`, eggs = `Egg count`, hatched = `Number Hatched`, fledged = `Number Fledged`, chicks = `Number of chicks seen`) # Define a function to extract the desired patterns and ensure enough columns extract_patterns <- function(text) { # Replace vertical bar '|' with an empty string text <- gsub("\\|", "", text) # Extract all 4 or 5-character long texts that start with R or r matches_r <- str_extract_all(text, "\\b[Rr]\\w{3,4}\\b")[[1]] # Extract UB, UN, UBF, UBM as standalone patterns matches_ub_un <- str_extract_all(text, "\\b[Uu][BbNnFfMm]\\b")[[1]] # Combine matches matches <- c(matches_r, matches_ub_un) # Ensure all matches are capitalized matches <- toupper(matches) # Ensure there are exactly two columns, filling with NA if necessary result <- c(matches, rep(NA, 2 - length(matches))) # Return the result as a named vector return(setNames(result, c("parent1", "parent2"))) } # Apply the function to the text column and store the results in new columns extracted_parents <- t(sapply(breeding_data_2021$NestID, extract_patterns)) breeding_data_2021 <- bind_cols(breeding_data_2021, extracted_parents) %>% select(-parent2) %>% rename(parent = parent1) %>% bind_rows(bind_cols(breeding_data_2021, extracted_parents) %>% select(-parent1) %>% rename(parent = parent2)) %>% filter(!is.na(parent)) %>% filter(parent %in% (dat %>% filter(season == 2021) %>% pull(rings_comb) %>% unique())) %>% filter(nest_fate == "Occupied" | as.numeric(chicks) > 0) %>% group_by(parent) %>% mutate( first_date_breeding = date[which.min(date)], last_date_breeding = date[which.max(date)]) %>% # arrange(desc(date)) %>% # slice(1) %>% ungroup() %>% # arrange(desc(date)) %>% mutate(nest_id = sub("^([^[:space:]]+).*", "\\1", NestID)) %>% # rename(last_date_breeding = date) %>% select(parent, first_date_breeding, last_date_breeding, nest_id, OBJECTID) # import and consolidate key columns breeding_data_2021_ <- read_excel("/Users/leberhart/ownCloud/kemp_projects/bdot/moult/data/Kaikoura_Dotterel_2021_Feb2022_upd_Nov22_Locations.xlsx", sheet = "Kaikoura_DotterelNest2021_0", col_types = "text") %>% mutate(hatch_date_ = as.POSIXct(as.numeric(`Date Hatched`) * 86400, origin = "1899-12-30", tz = "UTC"), fail_date_ = as.POSIXct(as.numeric(`Date Failed`) * 86400, origin = "1899-12-30", tz = "UTC"), found_date_ = as.POSIXct(as.numeric(`Date Found`) * 86400, origin = "1899-12-30", tz = "UTC")) %>% mutate(hatch_date_nz = with_tz(hatch_date_, tzone = "Pacific/Auckland"), fail_date_nz = with_tz(fail_date_, tzone = "Pacific/Auckland"), found_date_nz = with_tz(found_date_, tzone = "Pacific/Auckland")) %>% mutate(hatch_date = as.Date(hatch_date_nz), fail_date = as.Date(fail_date_nz), found_date = as.Date(found_date_nz)) %>% mutate(nest_id = sub("^([^[:space:]]+).*", "\\1", `Nest ID`)) %>% select(OBJECTID, nest_id, found_date, hatch_date, fail_date) breeding_2021_final <- left_join(breeding_data_2021, breeding_data_2021_, by = "nest_id") %>% mutate(date_check = ifelse((!is.na(found_date) & is.na(fail_date) & is.na(hatch_date)) | ((!is.na(hatch_date) & found_date < hatch_date)) | ((!is.na(fail_date) & !is.na(hatch_date)) & hatch_date < fail_date) | ((!is.na(fail_date) & found_date < fail_date)), 1, 0)) %>% group_by(parent) %>% mutate(last_date_breeding_ = max(found_date, hatch_date, fail_date, na.rm = TRUE)) %>% mutate(first_date_breeding_ = min(found_date, hatch_date, fail_date, na.rm = TRUE)) %>% mutate(days_diff = last_date_breeding_ - last_date_breeding) %>% mutate(days_diff = first_date_breeding_ - first_date_breeding) %>% arrange(desc(days_diff)) %>% mutate(last_date_breeding_final = as.Date(ifelse(days_diff > 0, last_date_breeding + ceiling(days_diff/2), last_date_breeding)), first_date_breeding_final = as.Date(ifelse(days_diff > 0, first_date_breeding + ceiling(days_diff/2), first_date_breeding))) %>% # arrange(desc(last_date_breeding_final)) %>% select(parent, first_date_breeding_final, last_date_breeding_final) %>% # specify the season as the first calender year mutate(season = ifelse(month(last_date_breeding_final) < 7, year(last_date_breeding_final) - 1, year(last_date_breeding_final))) %>% distinct() ########### ## 2022 season # import and consolidate key columns breeding_data_2022 <- read_excel("/Users/leberhart/ownCloud/kemp_projects/bdot/moult/data/Kaikoura_Dotterel_Jan23_upd.xlsx", sheet = "Kaikoura_NestVisit_1", col_types = "text") %>% mutate(visit_date_ = as.POSIXct(as.numeric(`Visit Date`) * 86400, origin = "1899-12-30", tz = "UTC")) %>% mutate(visit_date_nz = with_tz(visit_date_, tzone = "Pacific/Auckland")) %>% mutate(date = as.Date(visit_date_nz)) %>% select(OBJECTID, NestID, date, `Nest Status`, `Egg count`, `Number Hatched`, Notes, `Number Fledged`, `Number of chicks seen`) %>% rename(nest_fate = `Nest Status`, eggs = `Egg count`, hatched = `Number Hatched`, fledged = `Number Fledged`, chicks = `Number of chicks seen`) # Apply the function to the text column and store the results in new columns extracted_parents2 <- t(sapply(breeding_data_2022$NestID, extract_patterns)) breeding_data_2022 <- bind_cols(breeding_data_2022, extracted_parents2) %>% select(-parent2) %>% rename(parent = parent1) %>% bind_rows(bind_cols(breeding_data_2022, extracted_parents2) %>% select(-parent1) %>% rename(parent = parent2)) %>% filter(!is.na(parent)) %>% filter(parent %in% (dat %>% filter(season == 2022) %>% pull(rings_comb) %>% unique())) %>% filter(nest_fate == "Occupied" | as.numeric(chicks) > 0) %>% group_by(parent) %>% mutate( first_date_breeding = date[which.min(date)], last_date_breeding = date[which.max(date)]) %>% # arrange(desc(date)) %>% # slice(1) %>% ungroup() %>% # arrange(desc(date)) %>% mutate(nest_id = sub("^([^[:space:]]+).*", "\\1", NestID)) %>% # rename(last_date_breeding = date) %>% select(parent, first_date_breeding, last_date_breeding, nest_id, OBJECTID) # import and consolidate key columns breeding_data_2022_ <- read_excel("/Users/leberhart/ownCloud/kemp_projects/bdot/moult/data/Kaikoura_Dotterel_Jan23_upd.xlsx", sheet = "Kaikoura_DotterelNest_0", col_types = "text") %>% mutate(hatch_date_ = as.POSIXct(as.numeric(`Date Hatched`) * 86400, origin = "1899-12-30", tz = "UTC"), fail_date_ = as.POSIXct(as.numeric(`Date Failed`) * 86400, origin = "1899-12-30", tz = "UTC"), found_date_ = as.POSIXct(as.numeric(`Date Found`) * 86400, origin = "1899-12-30", tz = "UTC")) %>% mutate(hatch_date_nz = with_tz(hatch_date_, tzone = "Pacific/Auckland"), fail_date_nz = with_tz(fail_date_, tzone = "Pacific/Auckland"), found_date_nz = with_tz(found_date_, tzone = "Pacific/Auckland")) %>% mutate(hatch_date = as.Date(hatch_date_nz), fail_date = as.Date(fail_date_nz), found_date = as.Date(found_date_nz)) %>% mutate(nest_id = sub("^([^[:space:]]+).*", "\\1", `Nest ID`)) %>% select(OBJECTID, nest_id, found_date, hatch_date, fail_date) breeding_2022_final <- left_join(breeding_data_2022, breeding_data_2022_, by = "nest_id") %>% mutate(date_check = ifelse((!is.na(found_date) & is.na(fail_date) & is.na(hatch_date)) | ((!is.na(hatch_date) & found_date < hatch_date)) | ((!is.na(fail_date) & !is.na(hatch_date)) & hatch_date < fail_date) | ((!is.na(fail_date) & found_date < fail_date)), 1, 0)) %>% group_by(parent) %>% mutate(last_date_breeding_ = max(found_date, hatch_date, fail_date, na.rm = TRUE)) %>% mutate(first_date_breeding_ = min(found_date, hatch_date, fail_date, na.rm = TRUE)) %>% mutate(days_diff = last_date_breeding_ - last_date_breeding) %>% mutate(days_diff = first_date_breeding_ - first_date_breeding) %>% arrange(desc(days_diff)) %>% mutate(last_date_breeding_final = as.Date(ifelse(days_diff > 0, last_date_breeding + ceiling(days_diff/2), last_date_breeding)), first_date_breeding_final = as.Date(ifelse(days_diff > 0, first_date_breeding + ceiling(days_diff/2), first_date_breeding))) %>% # arrange(desc(last_date_breeding_final)) %>% select(parent, first_date_breeding_final, last_date_breeding_final) %>% # specify the season as the first calender year mutate(season = ifelse(month(last_date_breeding_final) < 7, year(last_date_breeding_final) - 1, year(last_date_breeding_final))) %>% distinct() ########### ## 2023 season # import and consolidate key columns breeding_data_2023 <- read_excel("/Users/leberhart/ownCloud/kemp_projects/bdot/moult/data/Kaikoura_Dotterel_Feb2024_upd.xlsx", sheet = "Kaikoura_NestVisit_1", col_types = "text") %>% mutate(visit_date_ = as.POSIXct(as.numeric(`Visit Date`) * 86400, origin = "1899-12-30", tz = "UTC")) %>% mutate(visit_date_nz = with_tz(visit_date_, tzone = "Pacific/Auckland")) %>% mutate(date = as.Date(visit_date_nz)) %>% select(OBJECTID, NestID, date, `Nest Status`, `Egg count`, `Number Hatched`, Notes, `Number Fledged`, `Number of chicks seen`) %>% rename(nest_fate = `Nest Status`, eggs = `Egg count`, hatched = `Number Hatched`, fledged = `Number Fledged`, chicks = `Number of chicks seen`) # Apply the function to the text column and store the results in new columns extracted_parents3 <- t(sapply(breeding_data_2023$NestID, extract_patterns)) breeding_data_2023 <- bind_cols(breeding_data_2023, extracted_parents3) %>% select(-parent2) %>% rename(parent = parent1) %>% bind_rows(bind_cols(breeding_data_2023, extracted_parents3) %>% select(-parent1) %>% rename(parent = parent2)) %>% filter(!is.na(parent)) %>% filter(parent %in% (dat %>% filter(season == 2023) %>% pull(rings_comb) %>% unique())) %>% filter(nest_fate == "Occupied" | as.numeric(chicks) > 0) %>% group_by(parent) %>% mutate( first_date_breeding = date[which.min(date)], last_date_breeding = date[which.max(date)]) %>% # arrange(desc(date)) %>% # slice(1) %>% ungroup() %>% # arrange(desc(date)) %>% mutate(nest_id = sub("^([^[:space:]]+).*", "\\1", NestID)) %>% # rename(last_date_breeding = date) %>% select(parent, first_date_breeding, last_date_breeding, nest_id, OBJECTID) # import and consolidate key columns breeding_data_2023_ <- read_excel("/Users/leberhart/ownCloud/kemp_projects/bdot/moult/data/Kaikoura_Dotterel_Feb2024_upd.xlsx", sheet = "Kaikoura_DotterelNest_0", col_types = "text") %>% mutate(hatch_date_ = as.POSIXct(as.numeric(`Date Hatched`) * 86400, origin = "1899-12-30", tz = "UTC"), fail_date_ = as.POSIXct(as.numeric(`Date Failed`) * 86400, origin = "1899-12-30", tz = "UTC"), found_date_ = as.POSIXct(as.numeric(`Date Found`) * 86400, origin = "1899-12-30", tz = "UTC")) %>% mutate(hatch_date_nz = with_tz(hatch_date_, tzone = "Pacific/Auckland"), fail_date_nz = with_tz(fail_date_, tzone = "Pacific/Auckland"), found_date_nz = with_tz(found_date_, tzone = "Pacific/Auckland")) %>% mutate(hatch_date = as.Date(hatch_date_nz), fail_date = as.Date(fail_date_nz), found_date = as.Date(found_date_nz)) %>% mutate(nest_id = sub("^([^[:space:]]+).*", "\\1", `Nest ID`)) %>% select(OBJECTID, nest_id, found_date, hatch_date, fail_date) breeding_2023_final <- left_join(breeding_data_2023, breeding_data_2023_, by = "nest_id") %>% mutate(date_check = ifelse((!is.na(found_date) & is.na(fail_date) & is.na(hatch_date)) | ((!is.na(hatch_date) & found_date < hatch_date)) | ((!is.na(fail_date) & !is.na(hatch_date)) & hatch_date < fail_date) | ((!is.na(fail_date) & found_date < fail_date)), 1, 0)) %>% group_by(parent) %>% mutate(last_date_breeding_ = max(found_date, hatch_date, fail_date, na.rm = TRUE)) %>% mutate(first_date_breeding_ = min(found_date, hatch_date, fail_date, na.rm = TRUE)) %>% mutate(days_diff = last_date_breeding_ - last_date_breeding) %>% mutate(days_diff = first_date_breeding_ - first_date_breeding) %>% arrange(desc(days_diff)) %>% mutate(last_date_breeding_final = as.Date(ifelse(days_diff > 0, last_date_breeding + ceiling(days_diff/2), last_date_breeding)), first_date_breeding_final = as.Date(ifelse(days_diff > 0, first_date_breeding + ceiling(days_diff/2), first_date_breeding))) %>% # arrange(desc(last_date_breeding_final)) %>% select(parent, first_date_breeding_final, last_date_breeding_final) %>% # specify the season as the first calender year mutate(season = ifelse(month(last_date_breeding_final) < 7, year(last_date_breeding_final) - 1, year(last_date_breeding_final))) %>% distinct() # combine breeding data from the 3 seasons into one table breeding_data_all <- bind_rows(breeding_2021_final,breeding_2022_final,breeding_2023_final) %>% rename(rings_comb = parent) %>% # change to Julian date shifted for the Southern Hemisphere (1 = July 1) mutate(last_breeding_date_J = as.numeric(format(last_date_breeding_final + 181, "%j")), first_breeding_date_J = as.numeric(format(first_date_breeding_final + 181, "%j"))) %>% ungroup() %>% # standardize the last breeding date by year mutate(last_breeding_date_J_s = scale_by(last_breeding_date_J ~ season, ., scale = 0), first_breeding_date_J_s = scale_by(first_breeding_date_J ~ season, ., scale = 0)) %>% # make the scaled date variable numeric class mutate(last_breeding_date_J_snum = as.numeric(last_breeding_date_J_s), first_breeding_date_J_snum = as.numeric(first_breeding_date_J_s)) ``` ### Relationships between breeding schedule and plumage ```{r, include=FALSE} ind_prop_molt_scores_last_breeding_date <- ind_prop_molt_scores %>% full_join(breeding_data_all, by = c("rings_comb", "season")) %>% mutate(date_diff = abs(last_breeding_date_J - date_J)) %>% arrange(rings_comb, season, date_diff) %>% group_by(rings_comb, season) %>% slice(1) %>% filter(date_diff < 28) %>% # duration of incubation (days) dplyr::select(rings_comb, season, sex, last_breeding_date_J, last_breeding_date_J_snum, prop_molt_score_max, max_breeding_score, age, migratory_status) %>% distinct() %>% group_by(sex) %>% mutate(std_max_breeding_score = (max_breeding_score - mean(max_breeding_score, na.rm = TRUE)) / sd(max_breeding_score, na.rm = TRUE)) %>% ungroup() %>% left_join(., moult_commence_ring_season %>% mutate(season = as.numeric(as.character(season))), by = c("rings_comb", "season")) ind_prop_molt_scores_first_breeding_date <- ind_prop_molt_scores %>% full_join(breeding_data_all, by = c("rings_comb", "season")) %>% mutate(date_diff = abs(first_breeding_date_J - date_J)) %>% arrange(rings_comb, season, date_diff) %>% group_by(rings_comb, season) %>% slice(1) %>% dplyr::select(rings_comb, season, sex, first_breeding_date_J, first_breeding_date_J_snum, prop_molt_score_max, max_breeding_score, age, migratory_status) %>% distinct() %>% group_by(sex) %>% mutate(std_max_breeding_score = (max_breeding_score - mean(max_breeding_score, na.rm = TRUE)) / sd(max_breeding_score, na.rm = TRUE)) %>% ungroup() %>% left_join(., moult_commence_ring_season %>% mutate(season = as.numeric(as.character(season))), by = c("rings_comb", "season")) ``` #### effect of breeding plumage score on prolonged breeding do individuals with a fuller breeding plumage (for their sex) breed later than the rest of the population? No effect ```{r} mod_max_score_last_breeding_date <- lme4::lmer(last_breeding_date_J_snum ~ std_max_breeding_score + (1 | rings_comb) + (1 | season), data = ind_prop_molt_scores_last_breeding_date) tbl_regression(mod_max_score_last_breeding_date, intercept = TRUE, label = list(std_max_breeding_score ~ "max. breeding plumage score (sex-scaled)")) ggplot() + geom_line(data = as.data.frame(allEffects(mod_max_score_last_breeding_date))$std_max_breeding_score, aes(x = std_max_breeding_score, y = fit)) + geom_ribbon(data = as.data.frame(allEffects(mod_max_score_last_breeding_date))$std_max_breeding_score, aes(x = std_max_breeding_score, ymax = upper, ymin = lower), lwd = 1, alpha = 0.25) + geom_point(data = ind_prop_molt_scores_last_breeding_date, aes(x = std_max_breeding_score, y = last_breeding_date_J_snum), alpha = 1, shape = 20) + theme(text = element_text(family = "Verdana"), axis.ticks = element_blank(), plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"), panel.spacing = unit(1, "cm"), panel.background = element_rect(fill = 'transparent', color = NA), plot.background = element_rect(fill = 'transparent', color = NA), panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"), panel.grid.minor = element_blank(), panel.grid.major.x = element_line(size = 0.25, lineend = "round", colour = "grey90")) + ylab("year-scaled date of last breeding attempt") + xlab("sex-scaled max. breeding plumage score") ``` #### effect of breeding plumage score on early breeding do individuals with a fuller breeding plumage (for their sex) breed earlier than the rest of the population? No effect ```{r} mod_max_score_first_breeding_date <- lme4::lmer(first_breeding_date_J_snum ~ std_max_breeding_score + (1 | rings_comb) + (1 | season), data = ind_prop_molt_scores_first_breeding_date) tbl_regression(mod_max_score_first_breeding_date, intercept = TRUE, label = list(std_max_breeding_score ~ "max. breeding plumage score (sex-scaled)")) ggplot() + geom_line(data = as.data.frame(allEffects(mod_max_score_first_breeding_date))$std_max_breeding_score, aes(x = std_max_breeding_score, y = fit)) + geom_ribbon(data = as.data.frame(allEffects(mod_max_score_first_breeding_date))$std_max_breeding_score, aes(x = std_max_breeding_score, ymax = upper, ymin = lower), lwd = 1, alpha = 0.25) + geom_point(data = ind_prop_molt_scores_first_breeding_date, aes(x = std_max_breeding_score, y = first_breeding_date_J_snum), alpha = 1, shape = 20) + theme(text = element_text(family = "Verdana"), axis.ticks = element_blank(), plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"), panel.spacing = unit(1, "cm"), panel.background = element_rect(fill = 'transparent', color = NA), plot.background = element_rect(fill = 'transparent', color = NA), panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"), panel.grid.minor = element_blank(), panel.grid.major.x = element_line(size = 0.25, lineend = "round", colour = "grey90")) + ylab("year-scaled date of first breeding attempt") + xlab("sex-scaled max. breeding plumage score") ``` #### effect of prolonged breeding on breeding plumage score do individuals with a late breeding attempts tend to have a fuller breeding plumage? No effect ```{r} mod_last_breeding_date_max_score <- lme4::lmer(std_max_breeding_score ~ last_breeding_date_J_snum + (1 | rings_comb) + (1 | season), data = ind_prop_molt_scores_last_breeding_date) tbl_regression(mod_last_breeding_date_max_score, intercept = TRUE, label = list(last_breeding_date_J_snum ~ "date last seen breeding (year-scaled)")) ggplot() + geom_line(data = as.data.frame(allEffects(mod_last_breeding_date_max_score))$last_breeding_date_J_snum, aes(x = last_breeding_date_J_snum, y = fit)) + geom_ribbon(data = as.data.frame(allEffects(mod_last_breeding_date_max_score))$last_breeding_date_J_snum, aes(x = last_breeding_date_J_snum, ymax = upper, ymin = lower), lwd = 1, alpha = 0.25) + geom_point(data = ind_prop_molt_scores_last_breeding_date, aes(x = last_breeding_date_J_snum, y = std_max_breeding_score), alpha = 1, shape = 20) + theme(text = element_text(family = "Verdana"), axis.ticks = element_blank(), plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"), panel.spacing = unit(1, "cm"), panel.background = element_rect(fill = 'transparent', color = NA), plot.background = element_rect(fill = 'transparent', color = NA), panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"), panel.grid.minor = element_blank(), panel.grid.major.x = element_line(size = 0.25, lineend = "round", colour = "grey90")) + xlab("year-scaled date of last breeding attempt") + ylab("sex-scaled max. breeding plumage score") ``` ### Relationships between breeding schedule and moult #### effect of prolonged breeding on moult do individuals with late breeding attempts have a delayed moult? No effect ```{r} mod_last_breeding_date_prop_molt_mid <- lmer(mid_point_date_J ~ last_breeding_date_J_snum + (1 | rings_comb) + (1 | season), data = ind_prop_molt_scores_last_breeding_date) tbl_regression(mod_last_breeding_date_prop_molt_mid, intercept = TRUE, label = list(last_breeding_date_J_snum ~ "date last seen breeding (year-scaled)")) ggplot() + geom_line(data = as.data.frame(allEffects(mod_last_breeding_date_prop_molt_mid))$last_breeding_date_J_snum, aes(x = last_breeding_date_J_snum, y = fit)) + geom_ribbon(data = as.data.frame(allEffects(mod_last_breeding_date_prop_molt_mid))$last_breeding_date_J_snum, aes(x = last_breeding_date_J_snum, ymax = upper, ymin = lower), lwd = 1, alpha = 0.25) + geom_point(data = ind_prop_molt_scores_last_breeding_date, aes(x = last_breeding_date_J_snum, y = mid_point_date_J), alpha = 1, shape = 20) + theme(text = element_text(family = "Verdana"), axis.ticks = element_blank(), plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"), panel.spacing = unit(1, "cm"), panel.background = element_rect(fill = 'transparent', color = NA), plot.background = element_rect(fill = 'transparent', color = NA), panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"), panel.grid.minor = element_blank(), panel.grid.major.x = element_line(size = 0.25, lineend = "round", colour = "grey90")) + xlab("year-scaled date of last breeding attempt") + ylab("mid-point date between\nlast max score photo and first photo during moult") ``` #### effect of prolonged breeding on prolonged retention of full plumage do individuals with late breeding attempts retain their full breeding plumage longer? There is a positive relationship between the date when an individual was last seen with its maximum breeding plumage and the date when it was last observed breeding. ```{r} mod_last_breeding_date_prop_molt_latest <- lmer(latest_max_date_J ~ last_breeding_date_J_snum + (1 | season), data = ind_prop_molt_scores_last_breeding_date) tbl_regression(mod_last_breeding_date_prop_molt_latest, intercept = TRUE, label = list(last_breeding_date_J_snum ~ "date last seen breeding (year-scaled)")) ggplot() + geom_line(data = as.data.frame(allEffects(mod_last_breeding_date_prop_molt_latest))$last_breeding_date_J_snum, aes(x = last_breeding_date_J_snum, y = fit)) + geom_ribbon(data = as.data.frame(allEffects(mod_last_breeding_date_prop_molt_latest))$last_breeding_date_J_snum, aes(x = last_breeding_date_J_snum, ymax = upper, ymin = lower), lwd = 1, alpha = 0.25) + geom_point(data = ind_prop_molt_scores_last_breeding_date, aes(x = last_breeding_date_J_snum, y = latest_max_date_J), alpha = 1, shape = 20) + theme(text = element_text(family = "Verdana"), axis.ticks = element_blank(), plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"), panel.spacing = unit(1, "cm"), panel.background = element_rect(fill = 'transparent', color = NA), plot.background = element_rect(fill = 'transparent', color = NA), panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"), panel.grid.minor = element_blank(), panel.grid.major.x = element_line(size = 0.25, lineend = "round", colour = "grey90")) + xlab("year-scaled date of last breeding attempt") + ylab("last date photographed with max score") ``` ### Relationships between age and breeding phenology #### effect of age on prolonged breeding do older individuals conclude their breeding season later? No ```{r} mod_age_last_breeding_date <- lme4::lmer(last_breeding_date_J_snum ~ age + (1 | rings_comb) + (1 | season), data = ind_prop_molt_scores_last_breeding_date) tbl_regression(mod_age_last_breeding_date, intercept = TRUE, label = list(age ~ "Age")) ggplot() + geom_line(data = as.data.frame(allEffects(mod_age_last_breeding_date))$age, aes(x = age, y = fit)) + geom_ribbon(data = as.data.frame(allEffects(mod_age_last_breeding_date))$age, aes(x = age, ymax = upper, ymin = lower), lwd = 1, alpha = 0.25) + geom_point(data = ind_prop_molt_scores_last_breeding_date, aes(x = age, y = last_breeding_date_J_snum), alpha = 1, shape = 20) + theme(text = element_text(family = "Verdana"), axis.ticks = element_blank(), plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"), panel.spacing = unit(1, "cm"), panel.background = element_rect(fill = 'transparent', color = NA), plot.background = element_rect(fill = 'transparent', color = NA), panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"), panel.grid.minor = element_blank(), panel.grid.major.x = element_line(size = 0.25, lineend = "round", colour = "grey90")) + xlab("age") + ylab("year-scaled date of last breeding attempt") ``` #### effect of age on early breeding do older individuals start breeding earlier? No ```{r} mod_age_first_breeding_date <- lme4::lmer(first_breeding_date_J_snum ~ age + (1 | rings_comb) + (1 | season), data = ind_prop_molt_scores_first_breeding_date) tbl_regression(mod_age_first_breeding_date, intercept = TRUE, label = list(age ~ "Age")) ggplot() + geom_line(data = as.data.frame(allEffects(mod_age_first_breeding_date))$age, aes(x = age, y = fit)) + geom_ribbon(data = as.data.frame(allEffects(mod_age_first_breeding_date))$age, aes(x = age, ymax = upper, ymin = lower), lwd = 1, alpha = 0.25) + geom_point(data = ind_prop_molt_scores_first_breeding_date, aes(x = age, y = first_breeding_date_J_snum), alpha = 1, shape = 20) + theme(text = element_text(family = "Verdana"), axis.ticks = element_blank(), plot.margin = unit(c(0.2,0.2,0.2,0.2), "cm"), panel.spacing = unit(1, "cm"), panel.background = element_rect(fill = 'transparent', color = NA), plot.background = element_rect(fill = 'transparent', color = NA), panel.grid.major.y = element_line(size = 0.25, lineend = "round", colour = "grey90"), panel.grid.minor = element_blank(), panel.grid.major.x = element_line(size = 0.25, lineend = "round", colour = "grey90")) + xlab("age") + ylab("year-scaled date of first breeding attempt") ``` ## Meeting notes: ### 13-Nov-24: Bart, Luke, and Bashar via zoom - add the breeding data to the analysis: for each individual include the date of last breeding (i.e., last date seen with nest or brood) as a predictor of molt timing. Also do an analysis relating the timing of last breeding to the age of the individual. Bashar will send Luke the breeding data he received from Ted. - drop pre-October data from the molt timing analysis. - next steps: Bashar to eventually write up this study as a report (which could eventually be used as his MSc thesis if he wants). Priority will be on him writing up a proposal for his thesis (which has a concrete deadline and is graded). Bashar will present the study at our weekly Monday meeting in the first few months of 2025. # Relevent papers to read and consider for manuscript - @gilbert2022late: Late breeding season definitive prebasic molt in males, and late breeding season brood care by females, in central California Wilson’s warblers - @drobniak2020seasonality: Seasonality, diet, and physiology as a predominant control factors of the moult dynamics in birds – a meta-analysis Figure 4 from their [pre-print](https://ecoevorxiv.org/repository/view/4403/) is relevant for the [age-specific](#within-individual-moult-dynamics-across-ages) timing we see: ```{r, echo=FALSE} knitr::include_graphics("/Users/leberhart/ownCloud/kemp_projects/bdot/moult/figs/Screenshot 2025-01-28 at 09.42.13.png") ``` - @thompson1983molt: Molt-breeding overlap and timing of pre-basic molt in Texas Least Terns - @barta2008optimal: Optimal moult strategies in migratory birds